Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App

In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.

Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [1]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))

Using TensorFlow backend.

There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [2]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))

There are 13233 total human images.

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [3]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()

Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [4]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

  • Number of human faces detected 99 out of 100 faces. 99.00% accuracy
  • Number of faces in dog images detected 11 out of 100 faces. 11.00% accuracy
In [5]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
human_faces = 0
dog_faces = 0
for human in human_files_short:
    human_faces += face_detector(human)
for dog in dog_files_short:
    dog_faces += face_detector(dog)
print('Number of human faces detected {} out of {} faces. {:.2%} accuracy'\
      .format(human_faces, len(human_files_short),(human_faces/len(human_files_short) )))
print('Number of faces in dog images detected {} out of {} faces. {:.2%} accuracy'\
      .format(dog_faces, len(dog_files_short),(dog_faces/len(dog_files_short) )))

Number of human faces detected 99 out of 100 faces. 99.00% accuracy
Number of faces in dog images detected 11 out of 100 faces. 11.00% accuracy

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer: I would think that training a network to recognize human figures/bodies would be a good first step. It seems that anything that we humans can do with our eyes, a CNN could approximate. It wouldn't be perfect.

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.

In [6]:
## (Optional) TODO: Report the performance of another  
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.

I built a CNN and trained it with the human faces. The performance was strange. At first it did pretty well with around 80% accuracy, but as I added more images, the accuracy went way down. The experiment can be found at: Face Recognition Experiment. I did notice that there were only a handful of images per dog breed and yet the network did fairly well. I had read that the more data, the better, but that is not what I found.

Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [7]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [8]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [9]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [10]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151))

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer: The detection of human faces in the dog images is to be expected since a few of them do contain both dog and human faces. For example: dogImages\train\049.Chinese_crested\Chinese_crested_03466.jpg

In [11]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_dogs = 0
dog_dogs = 0
for human in human_files_short:
    human_dogs += dog_detector(human)
for dog in dog_files_short:
    dog_dogs += dog_detector(dog)
print('Number of dogs in human files detected {} out of {} faces. {:.2%}'\
      .format(human_dogs, len(human_files_short),(human_dogs/len(human_files_short) )))
print('Number of dogs in dog files detected {} out of {} faces. {:.2%} accuracy'\
      .format(dog_dogs, len(dog_files_short),(dog_dogs/len(dog_files_short) )))

Number of dogs in human files detected 1 out of 100 faces. 1.00%
Number of dogs in dog files detected 100 out of 100 faces. 100.00% accuracy
In [12]:
img = cv2.imread(dog)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image
plt.imshow(cv_rgb)
plt.show()

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [13]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255

100%|█████████████████████████████████████████████████████████████████████████████| 6680/6680 [00:57<00:00, 115.45it/s]
100%|███████████████████████████████████████████████████████████████████████████████| 835/835 [00:06<00:00, 120.74it/s]
100%|███████████████████████████████████████████████████████████████████████████████| 836/836 [00:06<00:00, 125.79it/s]

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer: I chose to follow the hinted architecture above since it is a simplified version of the VGG model, which has a proven record of image classification. The initial model got around 10%. I then expanded on the model by adding additional sets of conv2D layers and an extra Dense layer. That doubled the accuracy and was the best I could get.

In [14]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.models import Sequential
from keras import optimizers

model = Sequential()

### TODO: Define your architecture.
model.add(Conv2D(32, (3, 3), input_shape=(224, 224,3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

#model.add(Conv2D(64, (3, 3)))
#model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(GlobalAveragePooling2D())

model.add(Dense(266))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(133))
model.add(Activation('softmax'))

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       
_________________________________________________________________
activation_50 (Activation)   (None, 222, 222, 32)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 220, 220, 32)      9248      
_________________________________________________________________
activation_51 (Activation)   (None, 220, 220, 32)      0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 218, 218, 32)      9248      
_________________________________________________________________
activation_52 (Activation)   (None, 218, 218, 32)      0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 109, 109, 32)      0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 107, 107, 64)      18496     
_________________________________________________________________
activation_53 (Activation)   (None, 107, 107, 64)      0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 105, 105, 64)      36928     
_________________________________________________________________
activation_54 (Activation)   (None, 105, 105, 64)      0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 52, 52, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 50, 50, 128)       73856     
_________________________________________________________________
activation_55 (Activation)   (None, 50, 50, 128)       0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 48, 48, 128)       147584    
_________________________________________________________________
activation_56 (Activation)   (None, 48, 48, 128)       0         
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 24, 24, 128)       0         
_________________________________________________________________
global_average_pooling2d_1 ( (None, 128)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 266)               34314     
_________________________________________________________________
activation_57 (Activation)   (None, 266)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 266)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               35511     
_________________________________________________________________
activation_58 (Activation)   (None, 133)               0         
=================================================================
Total params: 366,081.0
Trainable params: 366,081.0
Non-trainable params: 0.0
_________________________________________________________________
In [15]:
print(train_targets.shape)

(6680, 133)

Compile the Model

In [16]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [17]:
from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 40

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)

Train on 6680 samples, validate on 835 samples
Epoch 1/40
6660/6680 [============================>.] - ETA: 538s - loss: 4.8901 - acc: 0.0000e+00 - ETA: 292s - loss: 4.9035 - acc: 0.0000e+00 - ETA: 210s - loss: 4.9037 - acc: 0.0000e+00 - ETA: 168s - loss: 4.9000 - acc: 0.0125     - ETA: 144s - loss: 4.8979 - acc: 0.0100 - ETA: 127s - loss: 4.8964 - acc: 0.0083 - ETA: 115s - loss: 4.8958 - acc: 0.0071 - ETA: 107s - loss: 4.8949 - acc: 0.0125 - ETA: 100s - loss: 4.8947 - acc: 0.0111 - ETA: 94s - loss: 4.8936 - acc: 0.0100  - ETA: 89s - loss: 4.8925 - acc: 0.0091 - ETA: 85s - loss: 4.8917 - acc: 0.0083 - ETA: 82s - loss: 4.8928 - acc: 0.0077 - ETA: 79s - loss: 4.8927 - acc: 0.0107 - ETA: 77s - loss: 4.8928 - acc: 0.0100 - ETA: 75s - loss: 4.8926 - acc: 0.0094 - ETA: 73s - loss: 4.8924 - acc: 0.0088 - ETA: 71s - loss: 4.8927 - acc: 0.0083 - ETA: 69s - loss: 4.8923 - acc: 0.0079 - ETA: 68s - loss: 4.8922 - acc: 0.0075 - ETA: 67s - loss: 4.8917 - acc: 0.0095 - ETA: 65s - loss: 4.8914 - acc: 0.0091 - ETA: 64s - loss: 4.8918 - acc: 0.0087 - ETA: 63s - 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acc: 0.0095 - ETA: 4s - loss: 4.8860 - acc: 0.0095 - ETA: 4s - loss: 4.8860 - acc: 0.0094 - ETA: 4s - loss: 4.8860 - acc: 0.0094 - ETA: 4s - loss: 4.8860 - acc: 0.0095 - ETA: 4s - loss: 4.8859 - acc: 0.0095 - ETA: 4s - loss: 4.8859 - acc: 0.0095 - ETA: 4s - loss: 4.8858 - acc: 0.0094 - ETA: 3s - loss: 4.8858 - acc: 0.0094 - ETA: 3s - loss: 4.8857 - acc: 0.0095 - ETA: 3s - loss: 4.8858 - acc: 0.0095 - ETA: 3s - loss: 4.8858 - acc: 0.0095 - ETA: 3s - loss: 4.8860 - acc: 0.0095 - ETA: 3s - loss: 4.8861 - acc: 0.0094 - ETA: 2s - loss: 4.8861 - acc: 0.0094 - ETA: 2s - loss: 4.8861 - acc: 0.0094 - ETA: 2s - loss: 4.8861 - acc: 0.0095 - ETA: 2s - loss: 4.8862 - acc: 0.0095 - ETA: 2s - loss: 4.8862 - acc: 0.0094 - ETA: 2s - loss: 4.8863 - acc: 0.0094 - ETA: 2s - loss: 4.8862 - acc: 0.0094 - ETA: 1s - loss: 4.8862 - acc: 0.0093 - ETA: 1s - loss: 4.8863 - acc: 0.0093 - ETA: 1s - loss: 4.8863 - acc: 0.0093 - ETA: 1s - loss: 4.8863 - acc: 0.0094 - ETA: 1s - loss: 4.8863 - acc: 0.0094 - ETA: 1s - loss: 4.8864 - acc: 0.0094 - ETA: 1s - loss: 4.8862 - acc: 0.0093 - ETA: 0s - loss: 4.8861 - acc: 0.0093 - ETA: 0s - loss: 4.8861 - acc: 0.0093 - ETA: 0s - loss: 4.8861 - acc: 0.0092 - ETA: 0s - loss: 4.8861 - acc: 0.0094 - ETA: 0s - loss: 4.8861 - acc: 0.0093 - ETA: 0s - loss: 4.8861 - acc: 0.0093Epoch 00000: val_loss improved from inf to 4.87577, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 52s - loss: 4.8862 - acc: 0.0093 - val_loss: 4.8758 - val_acc: 0.0108
Epoch 2/40
6660/6680 [============================>.] - ETA: 46s - loss: 4.8687 - acc: 0.0000e+00 - ETA: 46s - loss: 4.8838 - acc: 0.0000e+00 - ETA: 47s - loss: 4.8876 - acc: 0.0000e+00 - ETA: 47s - loss: 4.8938 - acc: 0.0000e+00 - ETA: 47s - loss: 4.8937 - acc: 0.0000e+00 - ETA: 47s - loss: 4.8903 - acc: 0.0000e+00 - ETA: 46s - loss: 4.8857 - acc: 0.0000e+00 - ETA: 46s - loss: 4.8945 - acc: 0.0000e+00 - ETA: 46s - loss: 4.8893 - acc: 0.0056     - ETA: 46s - loss: 4.8893 - acc: 0.0050 - ETA: 46s - loss: 4.8890 - acc: 0.0045 - ETA: 46s - loss: 4.8850 - acc: 0.0083 - ETA: 46s - loss: 4.8862 - acc: 0.0077 - ETA: 46s - loss: 4.8815 - acc: 0.0071 - ETA: 45s - loss: 4.8765 - acc: 0.0067 - ETA: 45s - loss: 4.8769 - acc: 0.0063 - ETA: 45s - loss: 4.8779 - acc: 0.0088 - ETA: 45s - loss: 4.8773 - acc: 0.0083 - ETA: 45s - loss: 4.8764 - acc: 0.0079 - ETA: 45s - loss: 4.8771 - acc: 0.0075 - ETA: 45s - loss: 4.8752 - acc: 0.0071 - ETA: 44s - loss: 4.8719 - acc: 0.0068 - ETA: 44s - loss: 4.8698 - acc: 0.0065 - 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ETA: 4s - loss: 4.8740 - acc: 0.0108 - ETA: 4s - loss: 4.8740 - acc: 0.0108 - ETA: 4s - loss: 4.8739 - acc: 0.0108 - ETA: 4s - loss: 4.8739 - acc: 0.0109 - ETA: 4s - loss: 4.8739 - acc: 0.0109 - ETA: 4s - loss: 4.8740 - acc: 0.0108 - ETA: 4s - loss: 4.8742 - acc: 0.0108 - ETA: 3s - loss: 4.8742 - acc: 0.0107 - ETA: 3s - loss: 4.8743 - acc: 0.0107 - ETA: 3s - loss: 4.8745 - acc: 0.0107 - ETA: 3s - loss: 4.8745 - acc: 0.0106 - ETA: 3s - loss: 4.8743 - acc: 0.0106 - ETA: 3s - loss: 4.8743 - acc: 0.0106 - ETA: 3s - loss: 4.8742 - acc: 0.0105 - ETA: 2s - loss: 4.8740 - acc: 0.0105 - ETA: 2s - loss: 4.8739 - acc: 0.0105 - ETA: 2s - loss: 4.8740 - acc: 0.0104 - ETA: 2s - loss: 4.8742 - acc: 0.0104 - ETA: 2s - loss: 4.8741 - acc: 0.0104 - ETA: 2s - loss: 4.8741 - acc: 0.0103 - ETA: 2s - loss: 4.8741 - acc: 0.0103 - ETA: 1s - loss: 4.8742 - acc: 0.0103 - ETA: 1s - loss: 4.8742 - acc: 0.0102 - ETA: 1s - loss: 4.8743 - acc: 0.0102 - ETA: 1s - loss: 4.8741 - acc: 0.0103 - ETA: 1s - loss: 4.8742 - acc: 0.0103 - ETA: 1s - loss: 4.8741 - acc: 0.0104 - ETA: 1s - loss: 4.8741 - acc: 0.0104 - ETA: 0s - loss: 4.8741 - acc: 0.0104 - ETA: 0s - loss: 4.8741 - acc: 0.0103 - ETA: 0s - loss: 4.8740 - acc: 0.0103 - ETA: 0s - loss: 4.8740 - acc: 0.0103 - ETA: 0s - loss: 4.8742 - acc: 0.0102 - ETA: 0s - loss: 4.8741 - acc: 0.0102Epoch 00001: val_loss improved from 4.87577 to 4.86974, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.8742 - acc: 0.0102 - val_loss: 4.8697 - val_acc: 0.0108
Epoch 3/40
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acc: 0.0121 - ETA: 1s - loss: 4.8618 - acc: 0.0121 - ETA: 0s - loss: 4.8616 - acc: 0.0122 - ETA: 0s - loss: 4.8617 - acc: 0.0122 - ETA: 0s - loss: 4.8619 - acc: 0.0121 - ETA: 0s - loss: 4.8620 - acc: 0.0121 - ETA: 0s - loss: 4.8618 - acc: 0.0120 - ETA: 0s - loss: 4.8619 - acc: 0.0120Epoch 00002: val_loss improved from 4.86974 to 4.82420, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.8616 - acc: 0.0120 - val_loss: 4.8242 - val_acc: 0.0192
Epoch 4/40
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acc: 0.0163 - ETA: 1s - loss: 4.7926 - acc: 0.0162 - ETA: 0s - loss: 4.7921 - acc: 0.0163 - ETA: 0s - loss: 4.7918 - acc: 0.0164 - ETA: 0s - loss: 4.7913 - acc: 0.0164 - ETA: 0s - loss: 4.7910 - acc: 0.0163 - ETA: 0s - loss: 4.7910 - acc: 0.0163 - ETA: 0s - loss: 4.7911 - acc: 0.0162Epoch 00003: val_loss improved from 4.82420 to 4.74321, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.7908 - acc: 0.0165 - val_loss: 4.7432 - val_acc: 0.0204
Epoch 5/40
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acc: 0.0187 - ETA: 1s - loss: 4.7216 - acc: 0.0187 - ETA: 0s - loss: 4.7217 - acc: 0.0186 - ETA: 0s - loss: 4.7214 - acc: 0.0185 - ETA: 0s - loss: 4.7213 - acc: 0.0186 - ETA: 0s - loss: 4.7213 - acc: 0.0186 - ETA: 0s - loss: 4.7215 - acc: 0.0185 - ETA: 0s - loss: 4.7208 - acc: 0.0185Epoch 00004: val_loss improved from 4.74321 to 4.66896, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.7204 - acc: 0.0184 - val_loss: 4.6690 - val_acc: 0.0216
Epoch 6/40
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acc: 0.0207 - ETA: 1s - loss: 4.6747 - acc: 0.0206 - ETA: 0s - loss: 4.6751 - acc: 0.0206 - ETA: 0s - loss: 4.6752 - acc: 0.0205 - ETA: 0s - loss: 4.6753 - acc: 0.0205 - ETA: 0s - loss: 4.6753 - acc: 0.0204 - ETA: 0s - loss: 4.6752 - acc: 0.0205 - ETA: 0s - loss: 4.6754 - acc: 0.0206Epoch 00005: val_loss improved from 4.66896 to 4.63640, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.6754 - acc: 0.0208 - val_loss: 4.6364 - val_acc: 0.0192
Epoch 7/40
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ETA: 4s - loss: 4.6539 - acc: 0.0203 - ETA: 4s - loss: 4.6532 - acc: 0.0202 - ETA: 4s - loss: 4.6533 - acc: 0.0201 - ETA: 4s - loss: 4.6533 - acc: 0.0201 - ETA: 4s - loss: 4.6534 - acc: 0.0202 - ETA: 4s - loss: 4.6535 - acc: 0.0201 - ETA: 3s - loss: 4.6531 - acc: 0.0200 - ETA: 3s - loss: 4.6533 - acc: 0.0201 - ETA: 3s - loss: 4.6525 - acc: 0.0204 - ETA: 3s - loss: 4.6524 - acc: 0.0205 - ETA: 3s - loss: 4.6524 - acc: 0.0204 - ETA: 3s - loss: 4.6519 - acc: 0.0205 - ETA: 3s - loss: 4.6517 - acc: 0.0204 - ETA: 2s - loss: 4.6521 - acc: 0.0204 - ETA: 2s - loss: 4.6529 - acc: 0.0205 - ETA: 2s - loss: 4.6523 - acc: 0.0206 - ETA: 2s - loss: 4.6524 - acc: 0.0205 - ETA: 2s - loss: 4.6530 - acc: 0.0204 - ETA: 2s - loss: 4.6524 - acc: 0.0204 - ETA: 2s - loss: 4.6515 - acc: 0.0203 - ETA: 1s - loss: 4.6513 - acc: 0.0202 - ETA: 1s - loss: 4.6516 - acc: 0.0202 - ETA: 1s - loss: 4.6513 - acc: 0.0201 - ETA: 1s - loss: 4.6508 - acc: 0.0202 - ETA: 1s - loss: 4.6519 - acc: 0.0202 - ETA: 1s - loss: 4.6515 - acc: 0.0204 - ETA: 1s - loss: 4.6515 - acc: 0.0203 - ETA: 0s - loss: 4.6519 - acc: 0.0203 - ETA: 0s - loss: 4.6515 - acc: 0.0202 - ETA: 0s - loss: 4.6515 - acc: 0.0202 - ETA: 0s - loss: 4.6513 - acc: 0.0204 - ETA: 0s - loss: 4.6519 - acc: 0.0205 - ETA: 0s - loss: 4.6516 - acc: 0.0207Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 4.6516 - acc: 0.0207 - val_loss: 4.6797 - val_acc: 0.0228
Epoch 8/40
6660/6680 [============================>.] - ETA: 43s - loss: 4.8007 - acc: 0.0500 - ETA: 44s - loss: 4.6727 - acc: 0.0750 - ETA: 45s - loss: 4.6131 - acc: 0.0667 - ETA: 46s - loss: 4.5326 - acc: 0.0500 - ETA: 46s - loss: 4.5507 - acc: 0.0400 - ETA: 47s - loss: 4.6021 - acc: 0.0333 - ETA: 46s - loss: 4.5978 - acc: 0.0286 - ETA: 46s - loss: 4.6722 - acc: 0.0250 - ETA: 46s - loss: 4.6775 - acc: 0.0222 - ETA: 46s - loss: 4.6883 - acc: 0.0200 - ETA: 46s - loss: 4.6939 - acc: 0.0182 - ETA: 46s - loss: 4.6855 - acc: 0.0167 - ETA: 46s - loss: 4.6794 - acc: 0.0154 - ETA: 45s - loss: 4.6863 - acc: 0.0143 - ETA: 45s - loss: 4.7077 - acc: 0.0133 - ETA: 45s - loss: 4.6916 - acc: 0.0156 - ETA: 45s - loss: 4.6831 - acc: 0.0147 - ETA: 45s - loss: 4.6592 - acc: 0.0222 - ETA: 45s - loss: 4.6666 - acc: 0.0237 - ETA: 45s - loss: 4.6616 - acc: 0.0225 - ETA: 45s - loss: 4.6593 - acc: 0.0214 - ETA: 45s - loss: 4.6549 - acc: 0.0205 - ETA: 44s - loss: 4.6591 - acc: 0.0239 - ETA: 44s - loss: 4.6545 - acc: 0.0229 - 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acc: 0.0218 - ETA: 1s - loss: 4.6395 - acc: 0.0217 - ETA: 0s - loss: 4.6393 - acc: 0.0220 - ETA: 0s - loss: 4.6391 - acc: 0.0219 - ETA: 0s - loss: 4.6381 - acc: 0.0218 - ETA: 0s - loss: 4.6386 - acc: 0.0218 - ETA: 0s - loss: 4.6388 - acc: 0.0217 - ETA: 0s - loss: 4.6389 - acc: 0.0216Epoch 00007: val_loss improved from 4.63640 to 4.62788, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.6383 - acc: 0.0216 - val_loss: 4.6279 - val_acc: 0.0216
Epoch 9/40
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acc: 0.0267 - ETA: 1s - loss: 4.6262 - acc: 0.0266 - ETA: 0s - loss: 4.6268 - acc: 0.0265 - ETA: 0s - loss: 4.6266 - acc: 0.0264 - ETA: 0s - loss: 4.6270 - acc: 0.0264 - ETA: 0s - loss: 4.6263 - acc: 0.0266 - ETA: 0s - loss: 4.6259 - acc: 0.0267 - ETA: 0s - loss: 4.6266 - acc: 0.0267Epoch 00008: val_loss improved from 4.62788 to 4.62590, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.6261 - acc: 0.0268 - val_loss: 4.6259 - val_acc: 0.0192
Epoch 10/40
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acc: 0.0259 - ETA: 1s - loss: 4.6188 - acc: 0.0260 - ETA: 0s - loss: 4.6183 - acc: 0.0259 - ETA: 0s - loss: 4.6190 - acc: 0.0258 - ETA: 0s - loss: 4.6193 - acc: 0.0258 - ETA: 0s - loss: 4.6192 - acc: 0.0260 - ETA: 0s - loss: 4.6198 - acc: 0.0259 - ETA: 0s - loss: 4.6200 - acc: 0.0260Epoch 00009: val_loss improved from 4.62590 to 4.57887, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.6257 - acc: 0.0259 - val_loss: 4.5789 - val_acc: 0.0311
Epoch 11/40
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acc: 0.0296 - ETA: 1s - loss: 4.5702 - acc: 0.0295 - ETA: 0s - loss: 4.5697 - acc: 0.0297 - ETA: 0s - loss: 4.5696 - acc: 0.0296 - ETA: 0s - loss: 4.5702 - acc: 0.0295 - ETA: 0s - loss: 4.5708 - acc: 0.0295 - ETA: 0s - loss: 4.5702 - acc: 0.0297 - ETA: 0s - loss: 4.5713 - acc: 0.0296Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 4.5714 - acc: 0.0295 - val_loss: 4.6093 - val_acc: 0.0359
Epoch 12/40
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acc: 0.0339 - ETA: 1s - loss: 4.5036 - acc: 0.0339 - ETA: 0s - loss: 4.5032 - acc: 0.0338 - ETA: 0s - loss: 4.5042 - acc: 0.0340 - ETA: 0s - loss: 4.5037 - acc: 0.0341 - ETA: 0s - loss: 4.5034 - acc: 0.0341 - ETA: 0s - loss: 4.5036 - acc: 0.0340 - ETA: 0s - loss: 4.5038 - acc: 0.0344Epoch 00011: val_loss improved from 4.57887 to 4.43726, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.5036 - acc: 0.0344 - val_loss: 4.4373 - val_acc: 0.0311
Epoch 13/40
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acc: 0.0371 - ETA: 1s - loss: 4.4397 - acc: 0.0372 - ETA: 0s - loss: 4.4400 - acc: 0.0370 - ETA: 0s - loss: 4.4394 - acc: 0.0372 - ETA: 0s - loss: 4.4392 - acc: 0.0373 - ETA: 0s - loss: 4.4391 - acc: 0.0373 - ETA: 0s - loss: 4.4391 - acc: 0.0372 - ETA: 0s - loss: 4.4399 - acc: 0.0372Epoch 00012: val_loss improved from 4.43726 to 4.40043, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.4399 - acc: 0.0374 - val_loss: 4.4004 - val_acc: 0.0383
Epoch 14/40
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acc: 0.0399 - ETA: 1s - loss: 4.3834 - acc: 0.0398 - ETA: 0s - loss: 4.3835 - acc: 0.0398 - ETA: 0s - loss: 4.3822 - acc: 0.0400 - ETA: 0s - loss: 4.3816 - acc: 0.0398 - ETA: 0s - loss: 4.3821 - acc: 0.0397 - ETA: 0s - loss: 4.3832 - acc: 0.0396 - ETA: 0s - loss: 4.3824 - acc: 0.0398Epoch 00013: val_loss improved from 4.40043 to 4.34914, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.3828 - acc: 0.0397 - val_loss: 4.3491 - val_acc: 0.0467
Epoch 15/40
6660/6680 [============================>.] - ETA: 55s - loss: 3.9080 - acc: 0.0500 - ETA: 49s - loss: 4.1512 - acc: 0.0500 - ETA: 49s - loss: 4.1736 - acc: 0.0500 - ETA: 48s - loss: 4.2659 - acc: 0.0375 - ETA: 48s - loss: 4.2750 - acc: 0.0300 - ETA: 48s - loss: 4.2932 - acc: 0.0250 - ETA: 48s - loss: 4.3072 - acc: 0.0286 - ETA: 48s - loss: 4.2922 - acc: 0.0313 - ETA: 47s - loss: 4.2969 - acc: 0.0333 - ETA: 47s - loss: 4.3129 - acc: 0.0400 - ETA: 47s - loss: 4.3473 - acc: 0.0364 - ETA: 47s - loss: 4.3375 - acc: 0.0375 - ETA: 46s - loss: 4.3488 - acc: 0.0346 - ETA: 46s - loss: 4.3409 - acc: 0.0393 - ETA: 46s - loss: 4.3462 - acc: 0.0367 - ETA: 46s - loss: 4.3323 - acc: 0.0406 - ETA: 46s - loss: 4.3268 - acc: 0.0382 - ETA: 45s - loss: 4.3283 - acc: 0.0389 - ETA: 45s - loss: 4.3202 - acc: 0.0421 - ETA: 45s - loss: 4.3212 - acc: 0.0425 - ETA: 45s - loss: 4.3243 - acc: 0.0405 - ETA: 45s - loss: 4.3192 - acc: 0.0455 - ETA: 44s - loss: 4.3079 - acc: 0.0500 - ETA: 45s - loss: 4.3274 - acc: 0.0479 - 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acc: 0.0494 - ETA: 1s - loss: 4.3386 - acc: 0.0494 - ETA: 0s - loss: 4.3395 - acc: 0.0492 - ETA: 0s - loss: 4.3409 - acc: 0.0491 - ETA: 0s - loss: 4.3406 - acc: 0.0489 - ETA: 0s - loss: 4.3410 - acc: 0.0489 - ETA: 0s - loss: 4.3410 - acc: 0.0494 - ETA: 0s - loss: 4.3404 - acc: 0.0494Epoch 00014: val_loss improved from 4.34914 to 4.29384, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.3403 - acc: 0.0493 - val_loss: 4.2938 - val_acc: 0.0515
Epoch 16/40
6660/6680 [============================>.] - ETA: 53s - loss: 3.9137 - acc: 0.1500 - ETA: 52s - loss: 4.1813 - acc: 0.1000 - ETA: 50s - loss: 4.0355 - acc: 0.1167 - ETA: 49s - loss: 4.0356 - acc: 0.1000 - ETA: 49s - loss: 4.1538 - acc: 0.0800 - ETA: 48s - loss: 4.1894 - acc: 0.0750 - ETA: 48s - loss: 4.1856 - acc: 0.0714 - ETA: 48s - loss: 4.2195 - acc: 0.0750 - ETA: 47s - loss: 4.1827 - acc: 0.0833 - ETA: 47s - loss: 4.1836 - acc: 0.0750 - ETA: 47s - loss: 4.2097 - acc: 0.0727 - ETA: 46s - loss: 4.2746 - acc: 0.0708 - ETA: 46s - loss: 4.2934 - acc: 0.0654 - ETA: 46s - loss: 4.2800 - acc: 0.0643 - ETA: 46s - loss: 4.2823 - acc: 0.0633 - ETA: 46s - loss: 4.2716 - acc: 0.0594 - ETA: 46s - loss: 4.2867 - acc: 0.0559 - ETA: 45s - loss: 4.2963 - acc: 0.0556 - ETA: 45s - loss: 4.2996 - acc: 0.0526 - ETA: 45s - loss: 4.3015 - acc: 0.0525 - ETA: 45s - loss: 4.2860 - acc: 0.0524 - ETA: 45s - loss: 4.2804 - acc: 0.0523 - ETA: 44s - loss: 4.2875 - acc: 0.0500 - ETA: 44s - loss: 4.2833 - acc: 0.0500 - 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acc: 0.0525 - ETA: 1s - loss: 4.2812 - acc: 0.0523 - ETA: 0s - loss: 4.2821 - acc: 0.0521 - ETA: 0s - loss: 4.2807 - acc: 0.0523 - ETA: 0s - loss: 4.2823 - acc: 0.0521 - ETA: 0s - loss: 4.2828 - acc: 0.0520 - ETA: 0s - loss: 4.2823 - acc: 0.0518 - ETA: 0s - loss: 4.2828 - acc: 0.0517Epoch 00015: val_loss improved from 4.29384 to 4.27492, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.2829 - acc: 0.0515 - val_loss: 4.2749 - val_acc: 0.0551
Epoch 17/40
6660/6680 [============================>.] - ETA: 43s - loss: 3.6311 - acc: 0.1000 - ETA: 47s - loss: 4.0460 - acc: 0.0500 - ETA: 47s - loss: 4.0487 - acc: 0.0667 - ETA: 47s - loss: 4.1214 - acc: 0.0625 - ETA: 47s - loss: 4.0993 - acc: 0.0600 - ETA: 47s - loss: 4.0273 - acc: 0.0750 - ETA: 47s - loss: 4.0351 - acc: 0.0643 - ETA: 47s - loss: 4.0596 - acc: 0.0563 - ETA: 47s - loss: 4.0690 - acc: 0.0500 - ETA: 46s - loss: 4.0873 - acc: 0.0450 - ETA: 46s - loss: 4.0892 - acc: 0.0455 - ETA: 46s - loss: 4.0929 - acc: 0.0458 - ETA: 46s - loss: 4.1169 - acc: 0.0462 - ETA: 46s - loss: 4.1552 - acc: 0.0464 - ETA: 46s - loss: 4.1711 - acc: 0.0467 - ETA: 46s - loss: 4.1945 - acc: 0.0469 - ETA: 46s - loss: 4.2037 - acc: 0.0559 - ETA: 46s - loss: 4.2122 - acc: 0.0556 - ETA: 45s - loss: 4.2019 - acc: 0.0605 - ETA: 45s - loss: 4.1829 - acc: 0.0625 - ETA: 45s - loss: 4.1838 - acc: 0.0619 - ETA: 45s - loss: 4.1727 - acc: 0.0591 - ETA: 45s - loss: 4.1652 - acc: 0.0630 - ETA: 45s - loss: 4.1548 - acc: 0.0604 - 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ETA: 4s - loss: 4.2451 - acc: 0.0537 - ETA: 4s - loss: 4.2450 - acc: 0.0536 - ETA: 4s - loss: 4.2454 - acc: 0.0538 - ETA: 4s - loss: 4.2458 - acc: 0.0538 - ETA: 4s - loss: 4.2471 - acc: 0.0539 - ETA: 4s - loss: 4.2477 - acc: 0.0539 - ETA: 3s - loss: 4.2483 - acc: 0.0539 - ETA: 3s - loss: 4.2496 - acc: 0.0539 - ETA: 3s - loss: 4.2496 - acc: 0.0540 - ETA: 3s - loss: 4.2495 - acc: 0.0542 - ETA: 3s - loss: 4.2495 - acc: 0.0542 - ETA: 3s - loss: 4.2498 - acc: 0.0542 - ETA: 3s - loss: 4.2504 - acc: 0.0540 - ETA: 2s - loss: 4.2507 - acc: 0.0538 - ETA: 2s - loss: 4.2506 - acc: 0.0538 - ETA: 2s - loss: 4.2502 - acc: 0.0536 - ETA: 2s - loss: 4.2495 - acc: 0.0538 - ETA: 2s - loss: 4.2489 - acc: 0.0538 - ETA: 2s - loss: 4.2485 - acc: 0.0536 - ETA: 2s - loss: 4.2487 - acc: 0.0536 - ETA: 1s - loss: 4.2478 - acc: 0.0536 - ETA: 1s - loss: 4.2476 - acc: 0.0540 - ETA: 1s - loss: 4.2468 - acc: 0.0540 - ETA: 1s - loss: 4.2474 - acc: 0.0542 - ETA: 1s - loss: 4.2465 - acc: 0.0543 - ETA: 1s - loss: 4.2476 - acc: 0.0544 - ETA: 1s - loss: 4.2475 - acc: 0.0544 - ETA: 0s - loss: 4.2468 - acc: 0.0544 - ETA: 0s - loss: 4.2465 - acc: 0.0543 - ETA: 0s - loss: 4.2451 - acc: 0.0547 - ETA: 0s - loss: 4.2449 - acc: 0.0548 - ETA: 0s - loss: 4.2438 - acc: 0.0547 - ETA: 0s - loss: 4.2439 - acc: 0.0545Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 4.2446 - acc: 0.0543 - val_loss: 4.3749 - val_acc: 0.0443
Epoch 18/40
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acc: 0.0623 - ETA: 1s - loss: 4.1873 - acc: 0.0622 - ETA: 0s - loss: 4.1866 - acc: 0.0622 - ETA: 0s - loss: 4.1859 - acc: 0.0625 - ETA: 0s - loss: 4.1858 - acc: 0.0623 - ETA: 0s - loss: 4.1860 - acc: 0.0622 - ETA: 0s - loss: 4.1861 - acc: 0.0622 - ETA: 0s - loss: 4.1852 - acc: 0.0622Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 4.1858 - acc: 0.0623 - val_loss: 4.4084 - val_acc: 0.0587
Epoch 19/40
6660/6680 [============================>.] - ETA: 48s - loss: 4.6179 - acc: 0.1000 - ETA: 46s - loss: 4.3418 - acc: 0.0750 - ETA: 46s - loss: 4.2704 - acc: 0.0833 - ETA: 46s - loss: 4.2286 - acc: 0.0625 - ETA: 46s - loss: 4.1780 - acc: 0.0600 - ETA: 46s - loss: 4.2319 - acc: 0.0667 - ETA: 46s - loss: 4.2482 - acc: 0.0643 - ETA: 46s - loss: 4.2043 - acc: 0.0625 - ETA: 46s - loss: 4.1395 - acc: 0.0722 - ETA: 46s - loss: 4.1241 - acc: 0.0800 - ETA: 45s - loss: 4.1201 - acc: 0.0864 - ETA: 45s - loss: 4.1004 - acc: 0.0792 - ETA: 45s - loss: 4.1067 - acc: 0.0808 - ETA: 45s - loss: 4.0982 - acc: 0.0786 - ETA: 45s - loss: 4.1118 - acc: 0.0733 - ETA: 45s - loss: 4.1153 - acc: 0.0750 - ETA: 45s - loss: 4.1007 - acc: 0.0735 - ETA: 45s - loss: 4.1140 - acc: 0.0694 - ETA: 44s - loss: 4.1295 - acc: 0.0711 - ETA: 44s - loss: 4.1476 - acc: 0.0675 - ETA: 44s - loss: 4.1473 - acc: 0.0643 - ETA: 44s - loss: 4.1514 - acc: 0.0636 - ETA: 44s - loss: 4.1586 - acc: 0.0630 - ETA: 44s - loss: 4.1533 - acc: 0.0646 - 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ETA: 12s - loss: 4.1417 - acc: 0.0652 - ETA: 11s - loss: 4.1405 - acc: 0.0653 - ETA: 11s - loss: 4.1410 - acc: 0.0653 - ETA: 11s - loss: 4.1394 - acc: 0.0654 - ETA: 11s - loss: 4.1401 - acc: 0.0654 - ETA: 11s - loss: 4.1395 - acc: 0.0655 - ETA: 11s - loss: 4.1389 - acc: 0.0654 - ETA: 11s - loss: 4.1381 - acc: 0.0656 - ETA: 10s - loss: 4.1383 - acc: 0.0655 - ETA: 10s - loss: 4.1386 - acc: 0.0658 - ETA: 10s - loss: 4.1385 - acc: 0.0656 - ETA: 10s - loss: 4.1370 - acc: 0.0659 - ETA: 10s - loss: 4.1378 - acc: 0.0656 - ETA: 10s - loss: 4.1364 - acc: 0.0658 - ETA: 10s - loss: 4.1359 - acc: 0.0657 - ETA: 9s - loss: 4.1352 - acc: 0.0660  - ETA: 9s - loss: 4.1348 - acc: 0.0664 - ETA: 9s - loss: 4.1343 - acc: 0.0669 - ETA: 9s - loss: 4.1343 - acc: 0.0670 - ETA: 9s - loss: 4.1332 - acc: 0.0673 - ETA: 9s - loss: 4.1328 - acc: 0.0672 - ETA: 9s - loss: 4.1330 - acc: 0.0673 - ETA: 8s - loss: 4.1333 - acc: 0.0675 - ETA: 8s - loss: 4.1319 - acc: 0.0678 - ETA: 8s - loss: 4.1322 - acc: 0.0675 - ETA: 8s - loss: 4.1328 - acc: 0.0675 - ETA: 8s - loss: 4.1341 - acc: 0.0674 - ETA: 8s - loss: 4.1327 - acc: 0.0675 - ETA: 8s - loss: 4.1336 - acc: 0.0674 - ETA: 7s - loss: 4.1350 - acc: 0.0676 - ETA: 7s - loss: 4.1344 - acc: 0.0675 - ETA: 7s - loss: 4.1332 - acc: 0.0678 - ETA: 7s - loss: 4.1347 - acc: 0.0677 - ETA: 7s - loss: 4.1340 - acc: 0.0680 - ETA: 7s - loss: 4.1340 - acc: 0.0681 - ETA: 7s - loss: 4.1349 - acc: 0.0681 - ETA: 6s - loss: 4.1347 - acc: 0.0682 - ETA: 6s - loss: 4.1342 - acc: 0.0683 - ETA: 6s - loss: 4.1335 - acc: 0.0681 - ETA: 6s - loss: 4.1342 - acc: 0.0680 - ETA: 6s - loss: 4.1334 - acc: 0.0679 - ETA: 6s - loss: 4.1336 - acc: 0.0680 - ETA: 6s - loss: 4.1338 - acc: 0.0682 - ETA: 5s - loss: 4.1317 - acc: 0.0686 - ETA: 5s - loss: 4.1327 - acc: 0.0684 - ETA: 5s - loss: 4.1307 - acc: 0.0686 - ETA: 5s - loss: 4.1298 - acc: 0.0686 - ETA: 5s - loss: 4.1300 - acc: 0.0685 - ETA: 5s - loss: 4.1312 - acc: 0.0685 - ETA: 5s - loss: 4.1291 - acc: 0.0687 - ETA: 4s - loss: 4.1299 - acc: 0.0687 - ETA: 4s - loss: 4.1288 - acc: 0.0686 - ETA: 4s - loss: 4.1275 - acc: 0.0687 - ETA: 4s - loss: 4.1272 - acc: 0.0688 - ETA: 4s - loss: 4.1273 - acc: 0.0688 - ETA: 4s - loss: 4.1286 - acc: 0.0685 - ETA: 4s - loss: 4.1294 - acc: 0.0685 - ETA: 3s - loss: 4.1285 - acc: 0.0686 - ETA: 3s - loss: 4.1287 - acc: 0.0685 - ETA: 3s - loss: 4.1296 - acc: 0.0684 - ETA: 3s - loss: 4.1295 - acc: 0.0682 - ETA: 3s - loss: 4.1298 - acc: 0.0683 - ETA: 3s - loss: 4.1299 - acc: 0.0689 - ETA: 3s - loss: 4.1293 - acc: 0.0687 - ETA: 2s - loss: 4.1284 - acc: 0.0688 - ETA: 2s - loss: 4.1285 - acc: 0.0689 - ETA: 2s - loss: 4.1285 - acc: 0.0691 - ETA: 2s - loss: 4.1281 - acc: 0.0691 - ETA: 2s - loss: 4.1279 - acc: 0.0693 - ETA: 2s - loss: 4.1276 - acc: 0.0693 - ETA: 2s - loss: 4.1275 - acc: 0.0694 - ETA: 1s - loss: 4.1277 - acc: 0.0692 - ETA: 1s - loss: 4.1271 - acc: 0.0691 - ETA: 1s - loss: 4.1289 - acc: 0.0692 - ETA: 1s - loss: 4.1292 - acc: 0.0690 - ETA: 1s - loss: 4.1292 - acc: 0.0689 - ETA: 1s - loss: 4.1297 - acc: 0.0689 - ETA: 1s - loss: 4.1292 - acc: 0.0688 - ETA: 0s - loss: 4.1289 - acc: 0.0691 - ETA: 0s - loss: 4.1286 - acc: 0.0690 - ETA: 0s - loss: 4.1300 - acc: 0.0694 - ETA: 0s - loss: 4.1308 - acc: 0.0693 - ETA: 0s - loss: 4.1303 - acc: 0.0691 - ETA: 0s - loss: 4.1304 - acc: 0.0689Epoch 00018: val_loss improved from 4.27492 to 4.13950, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.1309 - acc: 0.0690 - val_loss: 4.1395 - val_acc: 0.0826
Epoch 20/40
6660/6680 [============================>.] - ETA: 50s - loss: 3.9264 - acc: 0.0500 - ETA: 47s - loss: 3.7598 - acc: 0.1000 - ETA: 48s - loss: 3.9974 - acc: 0.1000 - ETA: 48s - loss: 3.9573 - acc: 0.1000 - ETA: 47s - loss: 4.0549 - acc: 0.0800 - ETA: 47s - loss: 4.0882 - acc: 0.0750 - ETA: 47s - loss: 4.0635 - acc: 0.0714 - ETA: 47s - loss: 4.0146 - acc: 0.0750 - ETA: 46s - loss: 3.9952 - acc: 0.0722 - ETA: 46s - loss: 3.9509 - acc: 0.0850 - ETA: 46s - loss: 3.9776 - acc: 0.0864 - ETA: 46s - loss: 3.9423 - acc: 0.0958 - ETA: 46s - loss: 3.9368 - acc: 0.1000 - ETA: 46s - loss: 3.9226 - acc: 0.0964 - ETA: 46s - loss: 3.9099 - acc: 0.0967 - ETA: 45s - loss: 3.9179 - acc: 0.0906 - ETA: 45s - loss: 3.9022 - acc: 0.0882 - ETA: 45s - loss: 3.8973 - acc: 0.0972 - ETA: 45s - loss: 3.9053 - acc: 0.1053 - ETA: 45s - loss: 3.8910 - acc: 0.1050 - ETA: 45s - loss: 3.8934 - acc: 0.1024 - ETA: 44s - loss: 3.8939 - acc: 0.1000 - ETA: 44s - loss: 3.8931 - acc: 0.0978 - ETA: 44s - loss: 3.8864 - acc: 0.1000 - 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loss: 4.0550 - acc: 0.0758 - ETA: 8s - loss: 4.0548 - acc: 0.0759 - ETA: 8s - loss: 4.0536 - acc: 0.0758 - ETA: 8s - loss: 4.0536 - acc: 0.0757 - ETA: 7s - loss: 4.0522 - acc: 0.0758 - ETA: 7s - loss: 4.0520 - acc: 0.0757 - ETA: 7s - loss: 4.0532 - acc: 0.0756 - ETA: 7s - loss: 4.0535 - acc: 0.0757 - ETA: 7s - loss: 4.0541 - acc: 0.0758 - ETA: 7s - loss: 4.0544 - acc: 0.0757 - ETA: 7s - loss: 4.0547 - acc: 0.0756 - ETA: 6s - loss: 4.0550 - acc: 0.0757 - ETA: 6s - loss: 4.0543 - acc: 0.0756 - ETA: 6s - loss: 4.0530 - acc: 0.0755 - ETA: 6s - loss: 4.0537 - acc: 0.0753 - ETA: 6s - loss: 4.0533 - acc: 0.0752 - ETA: 6s - loss: 4.0525 - acc: 0.0754 - ETA: 6s - loss: 4.0509 - acc: 0.0755 - ETA: 5s - loss: 4.0516 - acc: 0.0754 - ETA: 5s - loss: 4.0529 - acc: 0.0753 - ETA: 5s - loss: 4.0526 - acc: 0.0751 - ETA: 5s - loss: 4.0530 - acc: 0.0750 - ETA: 5s - loss: 4.0542 - acc: 0.0747 - ETA: 5s - loss: 4.0529 - acc: 0.0753 - ETA: 5s - loss: 4.0529 - acc: 0.0753 - ETA: 4s - loss: 4.0530 - acc: 0.0752 - ETA: 4s - loss: 4.0546 - acc: 0.0751 - ETA: 4s - loss: 4.0543 - acc: 0.0750 - ETA: 4s - loss: 4.0541 - acc: 0.0751 - ETA: 4s - loss: 4.0528 - acc: 0.0750 - ETA: 4s - loss: 4.0534 - acc: 0.0751 - ETA: 4s - loss: 4.0535 - acc: 0.0750 - ETA: 3s - loss: 4.0536 - acc: 0.0751 - ETA: 3s - loss: 4.0537 - acc: 0.0748 - ETA: 3s - loss: 4.0536 - acc: 0.0748 - ETA: 3s - loss: 4.0522 - acc: 0.0750 - ETA: 3s - loss: 4.0544 - acc: 0.0748 - ETA: 3s - loss: 4.0552 - acc: 0.0745 - ETA: 3s - loss: 4.0552 - acc: 0.0743 - ETA: 2s - loss: 4.0561 - acc: 0.0740 - ETA: 2s - loss: 4.0553 - acc: 0.0743 - ETA: 2s - loss: 4.0549 - acc: 0.0744 - ETA: 2s - loss: 4.0556 - acc: 0.0743 - ETA: 2s - loss: 4.0562 - acc: 0.0741 - ETA: 2s - loss: 4.0563 - acc: 0.0740 - ETA: 2s - loss: 4.0560 - acc: 0.0742 - ETA: 1s - loss: 4.0563 - acc: 0.0743 - ETA: 1s - loss: 4.0553 - acc: 0.0742 - ETA: 1s - loss: 4.0559 - acc: 0.0741 - ETA: 1s - loss: 4.0558 - acc: 0.0742 - ETA: 1s - loss: 4.0557 - acc: 0.0742 - ETA: 1s - loss: 4.0554 - acc: 0.0741 - ETA: 1s - loss: 4.0554 - acc: 0.0743 - ETA: 0s - loss: 4.0553 - acc: 0.0745 - ETA: 0s - loss: 4.0557 - acc: 0.0746 - ETA: 0s - loss: 4.0558 - acc: 0.0744 - ETA: 0s - loss: 4.0571 - acc: 0.0742 - ETA: 0s - loss: 4.0564 - acc: 0.0741 - ETA: 0s - loss: 4.0570 - acc: 0.0742Epoch 00019: val_loss improved from 4.13950 to 4.02815, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.0587 - acc: 0.0741 - val_loss: 4.0282 - val_acc: 0.0874
Epoch 21/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.8934 - acc: 0.1000 - ETA: 47s - loss: 3.8503 - acc: 0.0750 - ETA: 49s - loss: 3.8166 - acc: 0.0667 - ETA: 49s - loss: 3.8828 - acc: 0.0625 - ETA: 48s - loss: 3.8903 - acc: 0.0700 - ETA: 48s - loss: 3.8581 - acc: 0.0833 - ETA: 47s - loss: 3.8695 - acc: 0.0857 - ETA: 47s - loss: 3.9476 - acc: 0.0875 - ETA: 46s - loss: 3.9874 - acc: 0.0833 - ETA: 46s - loss: 3.9605 - acc: 0.0850 - ETA: 46s - loss: 3.9810 - acc: 0.0773 - ETA: 46s - loss: 3.9693 - acc: 0.0792 - ETA: 46s - loss: 3.9755 - acc: 0.0808 - ETA: 45s - loss: 3.9325 - acc: 0.0893 - ETA: 45s - loss: 3.9695 - acc: 0.0833 - ETA: 45s - loss: 4.0163 - acc: 0.0781 - ETA: 45s - loss: 4.0145 - acc: 0.0794 - ETA: 45s - loss: 3.9987 - acc: 0.0861 - ETA: 45s - loss: 4.0187 - acc: 0.0842 - ETA: 44s - loss: 3.9998 - acc: 0.0850 - ETA: 44s - loss: 3.9998 - acc: 0.0810 - ETA: 44s - loss: 4.0022 - acc: 0.0795 - ETA: 44s - loss: 4.0257 - acc: 0.0804 - ETA: 44s - loss: 4.0233 - acc: 0.0833 - 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ETA: 12s - loss: 4.0042 - acc: 0.0840 - ETA: 11s - loss: 4.0032 - acc: 0.0841 - ETA: 11s - loss: 4.0041 - acc: 0.0841 - ETA: 11s - loss: 4.0040 - acc: 0.0842 - ETA: 11s - loss: 4.0033 - acc: 0.0843 - ETA: 11s - loss: 4.0038 - acc: 0.0841 - ETA: 11s - loss: 4.0042 - acc: 0.0842 - ETA: 11s - loss: 4.0045 - acc: 0.0840 - ETA: 10s - loss: 4.0046 - acc: 0.0843 - ETA: 10s - loss: 4.0065 - acc: 0.0844 - ETA: 10s - loss: 4.0068 - acc: 0.0846 - ETA: 10s - loss: 4.0070 - acc: 0.0845 - ETA: 10s - loss: 4.0106 - acc: 0.0842 - ETA: 10s - loss: 4.0115 - acc: 0.0840 - ETA: 10s - loss: 4.0113 - acc: 0.0841 - ETA: 9s - loss: 4.0111 - acc: 0.0842  - ETA: 9s - loss: 4.0101 - acc: 0.0844 - ETA: 9s - loss: 4.0100 - acc: 0.0843 - ETA: 9s - loss: 4.0088 - acc: 0.0847 - ETA: 9s - loss: 4.0081 - acc: 0.0846 - ETA: 9s - loss: 4.0082 - acc: 0.0846 - ETA: 9s - loss: 4.0088 - acc: 0.0843 - ETA: 8s - loss: 4.0089 - acc: 0.0842 - ETA: 8s - loss: 4.0099 - acc: 0.0841 - ETA: 8s - loss: 4.0103 - acc: 0.0839 - ETA: 8s - loss: 4.0095 - acc: 0.0838 - ETA: 8s - loss: 4.0099 - acc: 0.0841 - ETA: 8s - loss: 4.0103 - acc: 0.0839 - ETA: 8s - loss: 4.0097 - acc: 0.0838 - ETA: 7s - loss: 4.0117 - acc: 0.0835 - ETA: 7s - loss: 4.0133 - acc: 0.0832 - ETA: 7s - loss: 4.0144 - acc: 0.0833 - ETA: 7s - loss: 4.0147 - acc: 0.0832 - ETA: 7s - loss: 4.0141 - acc: 0.0834 - ETA: 7s - loss: 4.0124 - acc: 0.0838 - ETA: 7s - loss: 4.0149 - acc: 0.0837 - ETA: 6s - loss: 4.0148 - acc: 0.0841 - ETA: 6s - loss: 4.0146 - acc: 0.0843 - ETA: 6s - loss: 4.0133 - acc: 0.0842 - ETA: 6s - loss: 4.0118 - acc: 0.0844 - ETA: 6s - loss: 4.0128 - acc: 0.0841 - ETA: 6s - loss: 4.0146 - acc: 0.0838 - ETA: 6s - loss: 4.0144 - acc: 0.0836 - ETA: 5s - loss: 4.0138 - acc: 0.0833 - ETA: 5s - loss: 4.0130 - acc: 0.0835 - ETA: 5s - loss: 4.0131 - acc: 0.0837 - ETA: 5s - loss: 4.0121 - acc: 0.0836 - ETA: 5s - loss: 4.0110 - acc: 0.0837 - ETA: 5s - loss: 4.0119 - acc: 0.0836 - ETA: 5s - loss: 4.0113 - acc: 0.0834 - ETA: 4s - loss: 4.0128 - acc: 0.0833 - ETA: 4s - loss: 4.0133 - acc: 0.0834 - ETA: 4s - loss: 4.0137 - acc: 0.0836 - ETA: 4s - loss: 4.0151 - acc: 0.0837 - ETA: 4s - loss: 4.0158 - acc: 0.0836 - ETA: 4s - loss: 4.0166 - acc: 0.0834 - ETA: 4s - loss: 4.0160 - acc: 0.0835 - ETA: 3s - loss: 4.0145 - acc: 0.0839 - ETA: 3s - loss: 4.0153 - acc: 0.0836 - ETA: 3s - loss: 4.0167 - acc: 0.0837 - ETA: 3s - loss: 4.0170 - acc: 0.0835 - ETA: 3s - loss: 4.0164 - acc: 0.0836 - ETA: 3s - loss: 4.0169 - acc: 0.0833 - ETA: 3s - loss: 4.0177 - acc: 0.0831 - ETA: 2s - loss: 4.0178 - acc: 0.0830 - ETA: 2s - loss: 4.0180 - acc: 0.0830 - ETA: 2s - loss: 4.0177 - acc: 0.0829 - ETA: 2s - loss: 4.0177 - acc: 0.0831 - ETA: 2s - loss: 4.0183 - acc: 0.0830 - ETA: 2s - loss: 4.0178 - acc: 0.0831 - ETA: 2s - loss: 4.0167 - acc: 0.0831 - ETA: 1s - loss: 4.0169 - acc: 0.0832 - ETA: 1s - loss: 4.0182 - acc: 0.0832 - ETA: 1s - loss: 4.0187 - acc: 0.0834 - ETA: 1s - loss: 4.0187 - acc: 0.0833 - ETA: 1s - loss: 4.0186 - acc: 0.0834 - ETA: 1s - loss: 4.0184 - acc: 0.0831 - ETA: 1s - loss: 4.0171 - acc: 0.0833 - ETA: 0s - loss: 4.0172 - acc: 0.0831 - ETA: 0s - loss: 4.0158 - acc: 0.0831 - ETA: 0s - loss: 4.0160 - acc: 0.0830 - ETA: 0s - loss: 4.0156 - acc: 0.0829 - ETA: 0s - loss: 4.0150 - acc: 0.0828 - ETA: 0s - loss: 4.0148 - acc: 0.0827Epoch 00020: val_loss improved from 4.02815 to 4.02282, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 4.0145 - acc: 0.0829 - val_loss: 4.0228 - val_acc: 0.0826
Epoch 22/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.9032 - acc: 0.0500 - ETA: 47s - loss: 3.8056 - acc: 0.1250 - ETA: 47s - loss: 3.7875 - acc: 0.1000 - ETA: 47s - loss: 3.9133 - acc: 0.1000 - ETA: 47s - loss: 3.8843 - acc: 0.0800 - ETA: 47s - loss: 3.8541 - acc: 0.0833 - ETA: 47s - loss: 3.8479 - acc: 0.0857 - ETA: 46s - loss: 3.8709 - acc: 0.0750 - ETA: 47s - loss: 3.8159 - acc: 0.0778 - ETA: 47s - loss: 3.7946 - acc: 0.0700 - ETA: 47s - loss: 3.8395 - acc: 0.0682 - ETA: 46s - loss: 3.8419 - acc: 0.0708 - ETA: 46s - loss: 3.8892 - acc: 0.0692 - ETA: 46s - loss: 3.8792 - acc: 0.0714 - ETA: 46s - loss: 3.8712 - acc: 0.0733 - ETA: 46s - loss: 3.8658 - acc: 0.0781 - ETA: 45s - loss: 3.8655 - acc: 0.0794 - ETA: 45s - loss: 3.8649 - acc: 0.0833 - ETA: 45s - loss: 3.8431 - acc: 0.0789 - ETA: 45s - loss: 3.8689 - acc: 0.0800 - ETA: 45s - loss: 3.8918 - acc: 0.0786 - ETA: 45s - loss: 3.8957 - acc: 0.0864 - ETA: 44s - loss: 3.9073 - acc: 0.0870 - ETA: 44s - loss: 3.8998 - acc: 0.0833 - 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acc: 0.0870 - ETA: 1s - loss: 3.9477 - acc: 0.0869 - ETA: 0s - loss: 3.9487 - acc: 0.0867 - ETA: 0s - loss: 3.9486 - acc: 0.0869 - ETA: 0s - loss: 3.9475 - acc: 0.0870 - ETA: 0s - loss: 3.9461 - acc: 0.0873 - ETA: 0s - loss: 3.9457 - acc: 0.0870 - ETA: 0s - loss: 3.9449 - acc: 0.0871Epoch 00021: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.9454 - acc: 0.0871 - val_loss: 4.0675 - val_acc: 0.0838
Epoch 23/40
6660/6680 [============================>.] - ETA: 47s - loss: 3.2465 - acc: 0.1500 - ETA: 46s - loss: 3.5713 - acc: 0.1000 - ETA: 46s - loss: 3.6740 - acc: 0.1000 - ETA: 46s - loss: 3.7140 - acc: 0.1000 - ETA: 46s - loss: 3.6552 - acc: 0.0900 - ETA: 46s - loss: 3.7407 - acc: 0.0833 - ETA: 46s - loss: 3.7222 - acc: 0.0857 - ETA: 46s - loss: 3.7235 - acc: 0.0750 - ETA: 46s - loss: 3.7492 - acc: 0.0722 - ETA: 46s - loss: 3.7851 - acc: 0.0650 - ETA: 45s - loss: 3.8558 - acc: 0.0591 - ETA: 45s - loss: 3.8664 - acc: 0.0625 - ETA: 45s - loss: 3.8467 - acc: 0.0692 - ETA: 45s - loss: 3.8293 - acc: 0.0679 - ETA: 45s - loss: 3.8402 - acc: 0.0733 - ETA: 45s - loss: 3.8499 - acc: 0.0781 - ETA: 44s - loss: 3.8679 - acc: 0.0824 - ETA: 44s - loss: 3.8650 - acc: 0.0833 - ETA: 44s - loss: 3.8677 - acc: 0.0789 - ETA: 44s - loss: 3.8404 - acc: 0.0850 - ETA: 44s - loss: 3.8208 - acc: 0.0881 - ETA: 44s - loss: 3.8146 - acc: 0.0841 - ETA: 44s - loss: 3.8280 - acc: 0.0804 - ETA: 43s - loss: 3.8530 - acc: 0.0771 - 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acc: 0.0917 - ETA: 1s - loss: 3.9129 - acc: 0.0916 - ETA: 0s - loss: 3.9133 - acc: 0.0916 - ETA: 0s - loss: 3.9129 - acc: 0.0915 - ETA: 0s - loss: 3.9121 - acc: 0.0914 - ETA: 0s - loss: 3.9124 - acc: 0.0914 - ETA: 0s - loss: 3.9107 - acc: 0.0920 - ETA: 0s - loss: 3.9102 - acc: 0.0920Epoch 00022: val_loss improved from 4.02282 to 4.01132, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.9119 - acc: 0.0919 - val_loss: 4.0113 - val_acc: 0.0838
Epoch 24/40
6660/6680 [============================>.] - ETA: 47s - loss: 3.7458 - acc: 0.1000 - ETA: 47s - loss: 3.8589 - acc: 0.1000 - ETA: 48s - loss: 3.7946 - acc: 0.1000 - ETA: 48s - loss: 3.8085 - acc: 0.1125 - ETA: 47s - loss: 3.8394 - acc: 0.0900 - ETA: 47s - loss: 3.8962 - acc: 0.0833 - ETA: 47s - loss: 3.9476 - acc: 0.0714 - ETA: 47s - loss: 3.9314 - acc: 0.0750 - ETA: 46s - loss: 3.9516 - acc: 0.0667 - ETA: 46s - loss: 3.9323 - acc: 0.0650 - ETA: 46s - loss: 3.8798 - acc: 0.0909 - ETA: 45s - loss: 3.8628 - acc: 0.0917 - ETA: 45s - loss: 3.8707 - acc: 0.0923 - ETA: 45s - loss: 3.8699 - acc: 0.0964 - ETA: 45s - loss: 3.9218 - acc: 0.0933 - ETA: 45s - loss: 3.9285 - acc: 0.0875 - ETA: 45s - loss: 3.9148 - acc: 0.0853 - ETA: 44s - loss: 3.9212 - acc: 0.0889 - ETA: 44s - loss: 3.9071 - acc: 0.0868 - ETA: 44s - loss: 3.8881 - acc: 0.0900 - ETA: 44s - loss: 3.8841 - acc: 0.0905 - ETA: 44s - loss: 3.8889 - acc: 0.0886 - ETA: 44s - loss: 3.9114 - acc: 0.0848 - ETA: 44s - loss: 3.8931 - acc: 0.0854 - 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ETA: 4s - loss: 3.8598 - acc: 0.0940 - ETA: 4s - loss: 3.8591 - acc: 0.0940 - ETA: 4s - loss: 3.8583 - acc: 0.0941 - ETA: 4s - loss: 3.8567 - acc: 0.0946 - ETA: 4s - loss: 3.8564 - acc: 0.0943 - ETA: 4s - loss: 3.8562 - acc: 0.0943 - ETA: 3s - loss: 3.8546 - acc: 0.0945 - ETA: 3s - loss: 3.8542 - acc: 0.0942 - ETA: 3s - loss: 3.8556 - acc: 0.0940 - ETA: 3s - loss: 3.8559 - acc: 0.0939 - ETA: 3s - loss: 3.8586 - acc: 0.0937 - ETA: 3s - loss: 3.8597 - acc: 0.0938 - ETA: 3s - loss: 3.8604 - acc: 0.0936 - ETA: 2s - loss: 3.8603 - acc: 0.0935 - ETA: 2s - loss: 3.8609 - acc: 0.0938 - ETA: 2s - loss: 3.8600 - acc: 0.0938 - ETA: 2s - loss: 3.8599 - acc: 0.0935 - ETA: 2s - loss: 3.8592 - acc: 0.0936 - ETA: 2s - loss: 3.8577 - acc: 0.0940 - ETA: 2s - loss: 3.8572 - acc: 0.0938 - ETA: 1s - loss: 3.8580 - acc: 0.0938 - ETA: 1s - loss: 3.8577 - acc: 0.0938 - ETA: 1s - loss: 3.8577 - acc: 0.0940 - ETA: 1s - loss: 3.8570 - acc: 0.0941 - ETA: 1s - loss: 3.8563 - acc: 0.0943 - ETA: 1s - loss: 3.8561 - acc: 0.0948 - ETA: 1s - loss: 3.8559 - acc: 0.0946 - ETA: 0s - loss: 3.8562 - acc: 0.0948 - ETA: 0s - loss: 3.8569 - acc: 0.0945 - ETA: 0s - loss: 3.8565 - acc: 0.0948 - ETA: 0s - loss: 3.8562 - acc: 0.0950 - ETA: 0s - loss: 3.8560 - acc: 0.0952 - ETA: 0s - loss: 3.8548 - acc: 0.0953Epoch 00023: val_loss improved from 4.01132 to 3.97793, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.8542 - acc: 0.0954 - val_loss: 3.9779 - val_acc: 0.0850
Epoch 25/40
6660/6680 [============================>.] - ETA: 50s - loss: 4.2065 - acc: 0.1000 - ETA: 48s - loss: 4.2259 - acc: 0.0750 - ETA: 49s - loss: 4.2092 - acc: 0.0667 - ETA: 48s - loss: 4.1667 - acc: 0.1000 - ETA: 47s - loss: 4.1791 - acc: 0.0900 - ETA: 47s - loss: 4.0818 - acc: 0.0833 - ETA: 47s - loss: 4.0968 - acc: 0.0786 - ETA: 46s - loss: 4.0365 - acc: 0.0688 - ETA: 46s - loss: 4.0094 - acc: 0.0667 - ETA: 46s - loss: 4.0256 - acc: 0.0750 - ETA: 46s - loss: 4.0191 - acc: 0.0773 - ETA: 45s - loss: 3.9951 - acc: 0.0875 - ETA: 45s - loss: 3.9616 - acc: 0.0923 - ETA: 45s - loss: 3.9283 - acc: 0.1000 - ETA: 45s - loss: 3.9236 - acc: 0.1033 - ETA: 44s - loss: 3.9203 - acc: 0.1031 - ETA: 44s - loss: 3.9510 - acc: 0.0971 - ETA: 44s - loss: 3.9931 - acc: 0.0944 - ETA: 44s - loss: 3.9698 - acc: 0.0947 - ETA: 44s - loss: 3.9525 - acc: 0.0975 - ETA: 44s - loss: 3.9459 - acc: 0.0976 - ETA: 44s - loss: 3.9398 - acc: 0.0932 - ETA: 43s - loss: 3.9472 - acc: 0.0957 - ETA: 43s - loss: 3.9392 - acc: 0.0938 - 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acc: 0.1029 - ETA: 1s - loss: 3.8371 - acc: 0.1029 - ETA: 0s - loss: 3.8355 - acc: 0.1030 - ETA: 0s - loss: 3.8360 - acc: 0.1030 - ETA: 0s - loss: 3.8356 - acc: 0.1033 - ETA: 0s - loss: 3.8340 - acc: 0.1036 - ETA: 0s - loss: 3.8332 - acc: 0.1036 - ETA: 0s - loss: 3.8325 - acc: 0.1036Epoch 00024: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.8323 - acc: 0.1036 - val_loss: 3.9926 - val_acc: 0.0958
Epoch 26/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.3972 - acc: 0.0500 - ETA: 44s - loss: 3.4482 - acc: 0.1250 - ETA: 45s - loss: 3.7018 - acc: 0.1167 - ETA: 47s - loss: 3.7137 - acc: 0.1125 - ETA: 47s - loss: 3.6067 - acc: 0.1300 - ETA: 47s - loss: 3.5731 - acc: 0.1250 - ETA: 47s - loss: 3.6847 - acc: 0.1214 - ETA: 47s - loss: 3.6473 - acc: 0.1313 - ETA: 47s - loss: 3.7237 - acc: 0.1222 - ETA: 46s - loss: 3.7519 - acc: 0.1150 - ETA: 46s - loss: 3.7844 - acc: 0.1182 - ETA: 46s - loss: 3.7537 - acc: 0.1250 - ETA: 46s - loss: 3.7412 - acc: 0.1192 - ETA: 46s - loss: 3.7215 - acc: 0.1286 - ETA: 45s - loss: 3.7525 - acc: 0.1233 - ETA: 45s - loss: 3.7522 - acc: 0.1250 - ETA: 45s - loss: 3.7174 - acc: 0.1324 - ETA: 45s - loss: 3.7136 - acc: 0.1333 - ETA: 45s - loss: 3.7339 - acc: 0.1263 - ETA: 45s - loss: 3.7372 - acc: 0.1225 - ETA: 44s - loss: 3.7572 - acc: 0.1214 - ETA: 44s - loss: 3.7618 - acc: 0.1227 - ETA: 44s - loss: 3.8058 - acc: 0.1196 - ETA: 44s - loss: 3.8195 - acc: 0.1208 - 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acc: 0.1109 - ETA: 1s - loss: 3.7802 - acc: 0.1110 - ETA: 0s - loss: 3.7794 - acc: 0.1110 - ETA: 0s - loss: 3.7798 - acc: 0.1108 - ETA: 0s - loss: 3.7788 - acc: 0.1109 - ETA: 0s - loss: 3.7796 - acc: 0.1107 - ETA: 0s - loss: 3.7801 - acc: 0.1107 - ETA: 0s - loss: 3.7812 - acc: 0.1105Epoch 00025: val_loss improved from 3.97793 to 3.85743, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.7801 - acc: 0.1109 - val_loss: 3.8574 - val_acc: 0.0850
Epoch 27/40
6660/6680 [============================>.] - ETA: 43s - loss: 3.5744 - acc: 0.2000 - ETA: 47s - loss: 3.6200 - acc: 0.1250 - ETA: 47s - loss: 3.6697 - acc: 0.1000 - ETA: 47s - loss: 3.5447 - acc: 0.0875 - ETA: 46s - loss: 3.5566 - acc: 0.0900 - ETA: 46s - loss: 3.6239 - acc: 0.1000 - ETA: 46s - loss: 3.6222 - acc: 0.1000 - ETA: 46s - loss: 3.6529 - acc: 0.0938 - ETA: 46s - loss: 3.6461 - acc: 0.1000 - ETA: 46s - loss: 3.6314 - acc: 0.1050 - ETA: 46s - loss: 3.6233 - acc: 0.1091 - ETA: 46s - loss: 3.6268 - acc: 0.1125 - ETA: 46s - loss: 3.6468 - acc: 0.1077 - ETA: 45s - loss: 3.6325 - acc: 0.1036 - ETA: 45s - loss: 3.6124 - acc: 0.1100 - ETA: 45s - loss: 3.5893 - acc: 0.1156 - ETA: 45s - loss: 3.5963 - acc: 0.1176 - ETA: 45s - loss: 3.6306 - acc: 0.1111 - ETA: 45s - loss: 3.6424 - acc: 0.1184 - ETA: 45s - loss: 3.6602 - acc: 0.1175 - ETA: 45s - loss: 3.6469 - acc: 0.1214 - ETA: 44s - loss: 3.6235 - acc: 0.1250 - ETA: 44s - loss: 3.6215 - acc: 0.1261 - ETA: 44s - loss: 3.6217 - acc: 0.1271 - 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acc: 0.1232 - ETA: 1s - loss: 3.7238 - acc: 0.1232 - ETA: 0s - loss: 3.7231 - acc: 0.1236 - ETA: 0s - loss: 3.7224 - acc: 0.1236 - ETA: 0s - loss: 3.7217 - acc: 0.1235 - ETA: 0s - loss: 3.7196 - acc: 0.1237 - ETA: 0s - loss: 3.7193 - acc: 0.1236 - ETA: 0s - loss: 3.7192 - acc: 0.1236Epoch 00026: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.7199 - acc: 0.1237 - val_loss: 3.9648 - val_acc: 0.0910
Epoch 28/40
6660/6680 [============================>.] - ETA: 49s - loss: 4.0429 - acc: 0.1500 - ETA: 47s - loss: 3.7675 - acc: 0.1500 - ETA: 46s - loss: 3.8412 - acc: 0.1333 - ETA: 46s - loss: 3.8098 - acc: 0.1500 - ETA: 46s - loss: 3.8331 - acc: 0.1500 - ETA: 46s - loss: 3.8207 - acc: 0.1417 - ETA: 46s - loss: 3.8129 - acc: 0.1357 - ETA: 46s - loss: 3.8087 - acc: 0.1313 - ETA: 46s - loss: 3.7130 - acc: 0.1444 - ETA: 46s - loss: 3.6452 - acc: 0.1500 - ETA: 45s - loss: 3.6439 - acc: 0.1500 - ETA: 45s - loss: 3.6551 - acc: 0.1417 - ETA: 45s - loss: 3.6559 - acc: 0.1500 - ETA: 45s - loss: 3.6386 - acc: 0.1607 - ETA: 45s - loss: 3.6583 - acc: 0.1500 - ETA: 45s - loss: 3.6590 - acc: 0.1500 - ETA: 45s - loss: 3.6599 - acc: 0.1412 - ETA: 45s - loss: 3.6411 - acc: 0.1389 - ETA: 45s - loss: 3.6180 - acc: 0.1474 - ETA: 44s - loss: 3.6013 - acc: 0.1450 - ETA: 44s - loss: 3.5965 - acc: 0.1405 - ETA: 44s - loss: 3.6031 - acc: 0.1341 - ETA: 44s - loss: 3.5863 - acc: 0.1370 - ETA: 44s - loss: 3.5947 - acc: 0.1375 - 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acc: 0.1212 - ETA: 1s - loss: 3.6720 - acc: 0.1211 - ETA: 0s - loss: 3.6705 - acc: 0.1213 - ETA: 0s - loss: 3.6698 - acc: 0.1216 - ETA: 0s - loss: 3.6710 - acc: 0.1214 - ETA: 0s - loss: 3.6724 - acc: 0.1210 - ETA: 0s - loss: 3.6726 - acc: 0.1208 - ETA: 0s - loss: 3.6717 - acc: 0.1209Epoch 00027: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.6704 - acc: 0.1211 - val_loss: 3.8747 - val_acc: 0.1090
Epoch 29/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.8790 - acc: 0.1500 - ETA: 49s - loss: 4.0757 - acc: 0.0750 - ETA: 48s - loss: 3.8103 - acc: 0.1000 - ETA: 47s - loss: 3.7561 - acc: 0.1250 - ETA: 47s - loss: 3.5978 - acc: 0.1400 - ETA: 46s - loss: 3.5949 - acc: 0.1417 - ETA: 46s - loss: 3.5245 - acc: 0.1286 - ETA: 46s - loss: 3.4715 - acc: 0.1313 - ETA: 46s - loss: 3.4632 - acc: 0.1333 - ETA: 46s - loss: 3.4899 - acc: 0.1200 - ETA: 46s - loss: 3.4971 - acc: 0.1182 - ETA: 45s - loss: 3.5226 - acc: 0.1125 - ETA: 45s - loss: 3.5412 - acc: 0.1115 - ETA: 45s - loss: 3.5450 - acc: 0.1107 - ETA: 45s - loss: 3.5267 - acc: 0.1167 - ETA: 45s - loss: 3.5269 - acc: 0.1156 - ETA: 45s - loss: 3.5406 - acc: 0.1118 - ETA: 45s - loss: 3.5347 - acc: 0.1139 - ETA: 45s - loss: 3.5145 - acc: 0.1289 - ETA: 44s - loss: 3.5013 - acc: 0.1300 - ETA: 44s - loss: 3.4963 - acc: 0.1310 - ETA: 44s - loss: 3.5198 - acc: 0.1295 - ETA: 44s - loss: 3.5108 - acc: 0.1283 - ETA: 44s - loss: 3.5117 - acc: 0.1271 - 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acc: 0.1230 - ETA: 1s - loss: 3.6363 - acc: 0.1229 - ETA: 0s - loss: 3.6366 - acc: 0.1230 - ETA: 0s - loss: 3.6371 - acc: 0.1233 - ETA: 0s - loss: 3.6358 - acc: 0.1232 - ETA: 0s - loss: 3.6357 - acc: 0.1230 - ETA: 0s - loss: 3.6364 - acc: 0.1227 - ETA: 0s - loss: 3.6386 - acc: 0.1227Epoch 00028: val_loss improved from 3.85743 to 3.74694, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.6392 - acc: 0.1228 - val_loss: 3.7469 - val_acc: 0.1222
Epoch 30/40
6660/6680 [============================>.] - ETA: 51s - loss: 3.2601 - acc: 0.1500 - ETA: 49s - loss: 3.6726 - acc: 0.1000 - ETA: 48s - loss: 3.5539 - acc: 0.1167 - ETA: 48s - loss: 3.5470 - acc: 0.1250 - ETA: 47s - loss: 3.4862 - acc: 0.1300 - ETA: 47s - loss: 3.4991 - acc: 0.1333 - ETA: 47s - loss: 3.5048 - acc: 0.1214 - ETA: 47s - loss: 3.5198 - acc: 0.1125 - ETA: 47s - loss: 3.5139 - acc: 0.1222 - ETA: 46s - loss: 3.4974 - acc: 0.1250 - ETA: 46s - loss: 3.4577 - acc: 0.1273 - ETA: 46s - loss: 3.4520 - acc: 0.1292 - ETA: 46s - loss: 3.5097 - acc: 0.1269 - ETA: 46s - loss: 3.5064 - acc: 0.1357 - ETA: 46s - loss: 3.5271 - acc: 0.1267 - ETA: 46s - loss: 3.5278 - acc: 0.1313 - ETA: 45s - loss: 3.5183 - acc: 0.1324 - ETA: 45s - loss: 3.5365 - acc: 0.1250 - ETA: 45s - loss: 3.5362 - acc: 0.1211 - ETA: 45s - loss: 3.5200 - acc: 0.1250 - ETA: 45s - loss: 3.4941 - acc: 0.1286 - ETA: 45s - loss: 3.5062 - acc: 0.1295 - ETA: 44s - loss: 3.5271 - acc: 0.1283 - ETA: 44s - loss: 3.5336 - acc: 0.1292 - 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acc: 0.1298 - ETA: 1s - loss: 3.5752 - acc: 0.1297 - ETA: 0s - loss: 3.5779 - acc: 0.1294 - ETA: 0s - loss: 3.5802 - acc: 0.1295 - ETA: 0s - loss: 3.5795 - acc: 0.1295 - ETA: 0s - loss: 3.5786 - acc: 0.1296 - ETA: 0s - loss: 3.5787 - acc: 0.1297 - ETA: 0s - loss: 3.5776 - acc: 0.1299Epoch 00029: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.5783 - acc: 0.1295 - val_loss: 3.7781 - val_acc: 0.1150
Epoch 31/40
6660/6680 [============================>.] - ETA: 47s - loss: 3.7048 - acc: 0.2000 - ETA: 47s - loss: 3.4759 - acc: 0.1750 - ETA: 47s - loss: 3.5002 - acc: 0.1667 - ETA: 46s - loss: 3.3700 - acc: 0.2000 - ETA: 47s - loss: 3.4199 - acc: 0.1900 - ETA: 46s - loss: 3.3571 - acc: 0.2000 - ETA: 46s - loss: 3.4206 - acc: 0.1929 - ETA: 46s - loss: 3.4908 - acc: 0.1813 - ETA: 46s - loss: 3.4666 - acc: 0.1722 - ETA: 46s - loss: 3.4887 - acc: 0.1650 - ETA: 46s - loss: 3.4904 - acc: 0.1773 - ETA: 46s - loss: 3.4922 - acc: 0.1792 - ETA: 46s - loss: 3.4618 - acc: 0.1846 - ETA: 46s - loss: 3.4867 - acc: 0.1714 - ETA: 45s - loss: 3.5081 - acc: 0.1600 - ETA: 45s - loss: 3.4916 - acc: 0.1594 - ETA: 45s - loss: 3.4714 - acc: 0.1647 - ETA: 45s - loss: 3.4990 - acc: 0.1556 - ETA: 45s - loss: 3.4723 - acc: 0.1526 - ETA: 45s - loss: 3.4608 - acc: 0.1500 - ETA: 44s - loss: 3.4378 - acc: 0.1548 - ETA: 44s - loss: 3.4658 - acc: 0.1500 - ETA: 44s - loss: 3.4665 - acc: 0.1478 - ETA: 44s - loss: 3.4597 - acc: 0.1479 - 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acc: 0.1397 - ETA: 1s - loss: 3.5380 - acc: 0.1398 - ETA: 0s - loss: 3.5397 - acc: 0.1393 - ETA: 0s - loss: 3.5405 - acc: 0.1392 - ETA: 0s - loss: 3.5387 - acc: 0.1394 - ETA: 0s - loss: 3.5379 - acc: 0.1397 - ETA: 0s - loss: 3.5379 - acc: 0.1396 - ETA: 0s - loss: 3.5377 - acc: 0.1393Epoch 00030: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.5394 - acc: 0.1389 - val_loss: 3.7652 - val_acc: 0.1293
Epoch 32/40
6660/6680 [============================>.] - ETA: 48s - loss: 3.6386 - acc: 0.1500 - ETA: 48s - loss: 3.6035 - acc: 0.1250 - ETA: 48s - loss: 3.4742 - acc: 0.1333 - ETA: 47s - loss: 3.4226 - acc: 0.1125 - ETA: 47s - loss: 3.4342 - acc: 0.1200 - ETA: 47s - loss: 3.4786 - acc: 0.1333 - ETA: 47s - loss: 3.4213 - acc: 0.1214 - ETA: 46s - loss: 3.4063 - acc: 0.1188 - ETA: 46s - loss: 3.3883 - acc: 0.1389 - ETA: 46s - loss: 3.3686 - acc: 0.1350 - ETA: 46s - loss: 3.3632 - acc: 0.1364 - ETA: 46s - loss: 3.3519 - acc: 0.1375 - ETA: 45s - loss: 3.3229 - acc: 0.1500 - ETA: 46s - loss: 3.3371 - acc: 0.1464 - ETA: 45s - loss: 3.3759 - acc: 0.1433 - ETA: 45s - loss: 3.3559 - acc: 0.1500 - ETA: 45s - loss: 3.3537 - acc: 0.1529 - ETA: 45s - loss: 3.3049 - acc: 0.1611 - ETA: 44s - loss: 3.3196 - acc: 0.1658 - ETA: 44s - loss: 3.3277 - acc: 0.1575 - ETA: 44s - loss: 3.3321 - acc: 0.1548 - ETA: 44s - loss: 3.3271 - acc: 0.1568 - ETA: 44s - loss: 3.3130 - acc: 0.1652 - ETA: 44s - loss: 3.3374 - acc: 0.1646 - 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acc: 0.1475 - ETA: 1s - loss: 3.4892 - acc: 0.1471 - ETA: 0s - loss: 3.4900 - acc: 0.1474 - ETA: 0s - loss: 3.4893 - acc: 0.1474 - ETA: 0s - loss: 3.4883 - acc: 0.1474 - ETA: 0s - loss: 3.4886 - acc: 0.1474 - ETA: 0s - loss: 3.4873 - acc: 0.1474 - ETA: 0s - loss: 3.4891 - acc: 0.1474Epoch 00031: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.4886 - acc: 0.1479 - val_loss: 3.8785 - val_acc: 0.1078
Epoch 33/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.2759 - acc: 0.1500 - ETA: 47s - loss: 3.6559 - acc: 0.0750 - ETA: 47s - loss: 3.5669 - acc: 0.1167 - ETA: 47s - loss: 3.5918 - acc: 0.1375 - ETA: 46s - loss: 3.5771 - acc: 0.1300 - ETA: 46s - loss: 3.5670 - acc: 0.1333 - ETA: 46s - loss: 3.5770 - acc: 0.1214 - ETA: 46s - loss: 3.5386 - acc: 0.1188 - ETA: 46s - loss: 3.4576 - acc: 0.1333 - ETA: 46s - loss: 3.5719 - acc: 0.1200 - ETA: 46s - loss: 3.5477 - acc: 0.1136 - ETA: 46s - loss: 3.5730 - acc: 0.1083 - ETA: 46s - loss: 3.5197 - acc: 0.1154 - ETA: 46s - loss: 3.5247 - acc: 0.1250 - ETA: 45s - loss: 3.5180 - acc: 0.1300 - ETA: 45s - loss: 3.5070 - acc: 0.1375 - ETA: 45s - loss: 3.5142 - acc: 0.1324 - ETA: 45s - loss: 3.5048 - acc: 0.1306 - ETA: 45s - loss: 3.5239 - acc: 0.1342 - ETA: 45s - loss: 3.4996 - acc: 0.1375 - ETA: 45s - loss: 3.4653 - acc: 0.1500 - ETA: 45s - loss: 3.4698 - acc: 0.1477 - ETA: 44s - loss: 3.4677 - acc: 0.1457 - ETA: 44s - loss: 3.4615 - acc: 0.1458 - 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acc: 0.1483 - ETA: 1s - loss: 3.4527 - acc: 0.1483 - ETA: 0s - loss: 3.4511 - acc: 0.1488 - ETA: 0s - loss: 3.4521 - acc: 0.1488 - ETA: 0s - loss: 3.4524 - acc: 0.1489 - ETA: 0s - loss: 3.4519 - acc: 0.1489 - ETA: 0s - loss: 3.4526 - acc: 0.1491 - ETA: 0s - loss: 3.4545 - acc: 0.1486Epoch 00032: val_loss improved from 3.74694 to 3.63451, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.4526 - acc: 0.1490 - val_loss: 3.6345 - val_acc: 0.1425
Epoch 34/40
6660/6680 [============================>.] - ETA: 48s - loss: 3.3494 - acc: 0.2000 - ETA: 47s - loss: 3.3094 - acc: 0.1750 - ETA: 47s - loss: 3.2874 - acc: 0.1833 - ETA: 46s - loss: 3.3891 - acc: 0.1500 - ETA: 46s - loss: 3.3283 - acc: 0.1600 - ETA: 46s - loss: 3.3245 - acc: 0.1500 - ETA: 46s - loss: 3.3686 - acc: 0.1429 - ETA: 46s - loss: 3.3584 - acc: 0.1500 - ETA: 46s - loss: 3.3272 - acc: 0.1500 - ETA: 46s - loss: 3.3519 - acc: 0.1600 - ETA: 45s - loss: 3.3247 - acc: 0.1682 - ETA: 45s - loss: 3.3432 - acc: 0.1583 - ETA: 45s - loss: 3.3432 - acc: 0.1577 - ETA: 45s - loss: 3.3593 - acc: 0.1500 - ETA: 45s - loss: 3.3798 - acc: 0.1500 - ETA: 45s - loss: 3.3495 - acc: 0.1563 - ETA: 45s - loss: 3.3300 - acc: 0.1647 - ETA: 45s - loss: 3.3189 - acc: 0.1639 - ETA: 45s - loss: 3.2747 - acc: 0.1711 - ETA: 45s - loss: 3.2414 - acc: 0.1850 - ETA: 45s - loss: 3.2627 - acc: 0.1905 - ETA: 44s - loss: 3.2843 - acc: 0.1864 - ETA: 44s - loss: 3.3031 - acc: 0.1826 - ETA: 44s - loss: 3.3120 - acc: 0.1750 - 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acc: 0.1581 - ETA: 1s - loss: 3.4111 - acc: 0.1586 - ETA: 0s - loss: 3.4100 - acc: 0.1584 - ETA: 0s - loss: 3.4086 - acc: 0.1587 - ETA: 0s - loss: 3.4095 - acc: 0.1585 - ETA: 0s - loss: 3.4099 - acc: 0.1582 - ETA: 0s - loss: 3.4101 - acc: 0.1581 - ETA: 0s - loss: 3.4086 - acc: 0.1584Epoch 00033: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.4090 - acc: 0.1584 - val_loss: 3.7771 - val_acc: 0.1066
Epoch 35/40
6660/6680 [============================>.] - ETA: 50s - loss: 3.7662 - acc: 0.2500 - ETA: 53s - loss: 3.4443 - acc: 0.1750 - ETA: 51s - loss: 3.3771 - acc: 0.1500 - ETA: 49s - loss: 3.3684 - acc: 0.1250 - ETA: 49s - loss: 3.3300 - acc: 0.1300 - ETA: 48s - loss: 3.2497 - acc: 0.1583 - ETA: 48s - loss: 3.2570 - acc: 0.1500 - ETA: 47s - loss: 3.3135 - acc: 0.1563 - ETA: 47s - loss: 3.2708 - acc: 0.1611 - ETA: 47s - loss: 3.2130 - acc: 0.1700 - ETA: 46s - loss: 3.2466 - acc: 0.1545 - ETA: 46s - loss: 3.2637 - acc: 0.1708 - ETA: 46s - loss: 3.3071 - acc: 0.1654 - ETA: 46s - loss: 3.3330 - acc: 0.1571 - ETA: 46s - loss: 3.3490 - acc: 0.1633 - ETA: 45s - loss: 3.3301 - acc: 0.1625 - ETA: 45s - loss: 3.3084 - acc: 0.1647 - ETA: 45s - loss: 3.3187 - acc: 0.1639 - ETA: 45s - loss: 3.3641 - acc: 0.1632 - ETA: 45s - loss: 3.3201 - acc: 0.1775 - ETA: 45s - loss: 3.2874 - acc: 0.1762 - ETA: 44s - loss: 3.3091 - acc: 0.1795 - ETA: 44s - loss: 3.3275 - acc: 0.1761 - ETA: 44s - loss: 3.3343 - acc: 0.1729 - 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loss: 3.3445 - acc: 0.1645 - ETA: 8s - loss: 3.3428 - acc: 0.1650 - ETA: 8s - loss: 3.3426 - acc: 0.1650 - ETA: 8s - loss: 3.3438 - acc: 0.1644 - ETA: 7s - loss: 3.3437 - acc: 0.1645 - ETA: 7s - loss: 3.3420 - acc: 0.1648 - ETA: 7s - loss: 3.3413 - acc: 0.1646 - ETA: 7s - loss: 3.3407 - acc: 0.1647 - ETA: 7s - loss: 3.3418 - acc: 0.1641 - ETA: 7s - loss: 3.3431 - acc: 0.1643 - ETA: 7s - loss: 3.3432 - acc: 0.1642 - ETA: 6s - loss: 3.3440 - acc: 0.1640 - ETA: 6s - loss: 3.3437 - acc: 0.1641 - ETA: 6s - loss: 3.3431 - acc: 0.1644 - ETA: 6s - loss: 3.3417 - acc: 0.1649 - ETA: 6s - loss: 3.3411 - acc: 0.1647 - ETA: 6s - loss: 3.3420 - acc: 0.1644 - ETA: 6s - loss: 3.3425 - acc: 0.1640 - ETA: 5s - loss: 3.3427 - acc: 0.1642 - ETA: 5s - loss: 3.3429 - acc: 0.1646 - ETA: 5s - loss: 3.3423 - acc: 0.1649 - ETA: 5s - loss: 3.3430 - acc: 0.1644 - ETA: 5s - loss: 3.3429 - acc: 0.1648 - ETA: 5s - loss: 3.3435 - acc: 0.1649 - ETA: 5s - loss: 3.3425 - acc: 0.1651 - ETA: 4s - loss: 3.3417 - acc: 0.1650 - 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acc: 0.1661 - ETA: 1s - loss: 3.3448 - acc: 0.1659 - ETA: 0s - loss: 3.3435 - acc: 0.1663 - ETA: 0s - loss: 3.3433 - acc: 0.1661 - ETA: 0s - loss: 3.3458 - acc: 0.1656 - ETA: 0s - loss: 3.3446 - acc: 0.1654 - ETA: 0s - loss: 3.3450 - acc: 0.1654 - ETA: 0s - loss: 3.3471 - acc: 0.1650Epoch 00034: val_loss improved from 3.63451 to 3.59599, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.3467 - acc: 0.1650 - val_loss: 3.5960 - val_acc: 0.1437
Epoch 36/40
6660/6680 [============================>.] - ETA: 50s - loss: 3.0436 - acc: 0.3000 - ETA: 48s - loss: 3.2906 - acc: 0.3250 - ETA: 47s - loss: 3.4488 - acc: 0.2667 - ETA: 47s - loss: 3.3618 - acc: 0.2625 - ETA: 48s - loss: 3.3879 - acc: 0.2500 - ETA: 47s - loss: 3.3243 - acc: 0.2333 - ETA: 47s - loss: 3.2633 - acc: 0.2429 - ETA: 47s - loss: 3.3392 - acc: 0.2188 - ETA: 46s - loss: 3.3508 - acc: 0.2167 - ETA: 46s - loss: 3.3887 - acc: 0.2150 - ETA: 46s - loss: 3.3859 - acc: 0.2045 - ETA: 46s - loss: 3.3325 - acc: 0.2042 - ETA: 45s - loss: 3.2869 - acc: 0.2115 - ETA: 45s - loss: 3.2854 - acc: 0.2107 - ETA: 45s - loss: 3.3216 - acc: 0.1967 - ETA: 45s - loss: 3.3466 - acc: 0.1969 - ETA: 45s - loss: 3.3080 - acc: 0.2000 - ETA: 45s - loss: 3.2948 - acc: 0.2083 - ETA: 44s - loss: 3.3129 - acc: 0.2053 - ETA: 44s - loss: 3.3082 - acc: 0.2050 - ETA: 44s - loss: 3.3080 - acc: 0.2048 - ETA: 44s - loss: 3.2933 - acc: 0.2045 - ETA: 44s - loss: 3.2793 - acc: 0.2109 - ETA: 44s - loss: 3.3108 - acc: 0.2104 - 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acc: 0.1709 - ETA: 1s - loss: 3.3157 - acc: 0.1711 - ETA: 0s - loss: 3.3163 - acc: 0.1710 - ETA: 0s - loss: 3.3164 - acc: 0.1710 - ETA: 0s - loss: 3.3165 - acc: 0.1714 - ETA: 0s - loss: 3.3184 - acc: 0.1710 - ETA: 0s - loss: 3.3170 - acc: 0.1712 - ETA: 0s - loss: 3.3164 - acc: 0.1713Epoch 00035: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.3153 - acc: 0.1716 - val_loss: 3.6151 - val_acc: 0.1593
Epoch 37/40
6660/6680 [============================>.] - ETA: 51s - loss: 3.3676 - acc: 0.1500 - ETA: 48s - loss: 3.5783 - acc: 0.1500 - ETA: 48s - loss: 3.5654 - acc: 0.1000 - ETA: 47s - loss: 3.3665 - acc: 0.1625 - ETA: 47s - loss: 3.3543 - acc: 0.1400 - ETA: 46s - loss: 3.3628 - acc: 0.1417 - ETA: 46s - loss: 3.3237 - acc: 0.1500 - ETA: 46s - loss: 3.2661 - acc: 0.1688 - ETA: 46s - loss: 3.2368 - acc: 0.1889 - ETA: 46s - loss: 3.2140 - acc: 0.2000 - ETA: 46s - loss: 3.1641 - acc: 0.2091 - ETA: 46s - loss: 3.1418 - acc: 0.2167 - ETA: 46s - loss: 3.1788 - acc: 0.2192 - ETA: 45s - loss: 3.2095 - acc: 0.2071 - ETA: 45s - loss: 3.2348 - acc: 0.2000 - ETA: 45s - loss: 3.2661 - acc: 0.1938 - ETA: 45s - loss: 3.3108 - acc: 0.1882 - ETA: 45s - loss: 3.3409 - acc: 0.1861 - ETA: 44s - loss: 3.3094 - acc: 0.1868 - ETA: 44s - loss: 3.3386 - acc: 0.1850 - ETA: 44s - loss: 3.3490 - acc: 0.1786 - ETA: 44s - loss: 3.3522 - acc: 0.1795 - ETA: 44s - loss: 3.3452 - acc: 0.1848 - ETA: 44s - loss: 3.3291 - acc: 0.1854 - 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acc: 0.1816 - ETA: 0s - loss: 3.2680 - acc: 0.1818 - ETA: 0s - loss: 3.2680 - acc: 0.1819 - ETA: 0s - loss: 3.2697 - acc: 0.1813 - ETA: 0s - loss: 3.2687 - acc: 0.1814 - ETA: 0s - loss: 3.2708 - acc: 0.1810 - ETA: 0s - loss: 3.2713 - acc: 0.1809 - ETA: 0s - loss: 3.2712 - acc: 0.1808Epoch 00036: val_loss did not improve
6680/6680 [==============================] - 49s - loss: 3.2713 - acc: 0.1805 - val_loss: 3.6174 - val_acc: 0.1569
Epoch 38/40
6660/6680 [============================>.] - ETA: 43s - loss: 3.2816 - acc: 0.1500 - ETA: 46s - loss: 3.0597 - acc: 0.2000 - ETA: 46s - loss: 3.1395 - acc: 0.1833 - ETA: 46s - loss: 3.1340 - acc: 0.2000 - ETA: 46s - loss: 3.2106 - acc: 0.1800 - ETA: 46s - loss: 3.2226 - acc: 0.1917 - ETA: 47s - loss: 3.2753 - acc: 0.1786 - ETA: 47s - loss: 3.2449 - acc: 0.1813 - ETA: 47s - loss: 3.1772 - acc: 0.1778 - ETA: 46s - loss: 3.1667 - acc: 0.1800 - ETA: 46s - loss: 3.1212 - acc: 0.1909 - ETA: 46s - loss: 3.1340 - acc: 0.1958 - ETA: 46s - loss: 3.1175 - acc: 0.2000 - ETA: 46s - loss: 3.1336 - acc: 0.1929 - ETA: 45s - loss: 3.1230 - acc: 0.1967 - ETA: 45s - loss: 3.1163 - acc: 0.2000 - ETA: 45s - loss: 3.1370 - acc: 0.1971 - ETA: 45s - loss: 3.1369 - acc: 0.2028 - ETA: 45s - loss: 3.1512 - acc: 0.2053 - ETA: 45s - loss: 3.1599 - acc: 0.2075 - ETA: 44s - loss: 3.1742 - acc: 0.2095 - ETA: 44s - loss: 3.1595 - acc: 0.2068 - ETA: 44s - loss: 3.1768 - acc: 0.2000 - ETA: 44s - loss: 3.1327 - acc: 0.2063 - 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acc: 0.1934 - ETA: 1s - loss: 3.2191 - acc: 0.1936 - ETA: 0s - loss: 3.2205 - acc: 0.1933 - ETA: 0s - loss: 3.2197 - acc: 0.1933 - ETA: 0s - loss: 3.2191 - acc: 0.1932 - ETA: 0s - loss: 3.2202 - acc: 0.1931 - ETA: 0s - loss: 3.2209 - acc: 0.1929 - ETA: 0s - loss: 3.2209 - acc: 0.1929Epoch 00037: val_loss improved from 3.59599 to 3.58202, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.2223 - acc: 0.1931 - val_loss: 3.5820 - val_acc: 0.1461
Epoch 39/40
6660/6680 [============================>.] - ETA: 49s - loss: 3.4347 - acc: 0.0500 - ETA: 47s - loss: 3.1232 - acc: 0.1750 - ETA: 48s - loss: 3.1563 - acc: 0.1833 - ETA: 48s - loss: 3.0989 - acc: 0.1750 - ETA: 47s - loss: 3.0389 - acc: 0.2000 - ETA: 47s - loss: 3.1823 - acc: 0.2000 - ETA: 47s - loss: 3.0857 - acc: 0.2071 - ETA: 47s - loss: 3.0485 - acc: 0.2063 - ETA: 46s - loss: 3.0577 - acc: 0.1944 - ETA: 46s - loss: 3.0529 - acc: 0.2050 - ETA: 46s - loss: 3.0263 - acc: 0.2045 - ETA: 46s - loss: 3.0630 - acc: 0.1917 - ETA: 45s - loss: 3.0887 - acc: 0.1885 - ETA: 45s - loss: 3.1072 - acc: 0.1964 - ETA: 45s - loss: 3.1242 - acc: 0.1900 - ETA: 45s - loss: 3.1209 - acc: 0.1875 - ETA: 45s - loss: 3.1350 - acc: 0.1824 - ETA: 45s - loss: 3.1330 - acc: 0.1889 - ETA: 45s - loss: 3.1287 - acc: 0.1868 - ETA: 45s - loss: 3.1183 - acc: 0.1950 - ETA: 44s - loss: 3.1030 - acc: 0.1952 - ETA: 44s - loss: 3.1127 - acc: 0.2023 - ETA: 44s - loss: 3.0993 - acc: 0.2065 - ETA: 44s - loss: 3.0975 - acc: 0.2083 - 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acc: 0.1983 - ETA: 1s - loss: 3.1733 - acc: 0.1983 - ETA: 0s - loss: 3.1735 - acc: 0.1982 - ETA: 0s - loss: 3.1727 - acc: 0.1985 - ETA: 0s - loss: 3.1740 - acc: 0.1980 - ETA: 0s - loss: 3.1742 - acc: 0.1980 - ETA: 0s - loss: 3.1759 - acc: 0.1977 - ETA: 0s - loss: 3.1767 - acc: 0.1973Epoch 00038: val_loss improved from 3.58202 to 3.48691, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 50s - loss: 3.1746 - acc: 0.1978 - val_loss: 3.4869 - val_acc: 0.1617
Epoch 40/40
6660/6680 [============================>.] - ETA: 47s - loss: 2.8692 - acc: 0.1500 - ETA: 47s - loss: 3.0536 - acc: 0.2000 - ETA: 48s - loss: 2.8355 - acc: 0.2333 - ETA: 48s - loss: 2.8220 - acc: 0.2250 - ETA: 47s - loss: 2.9469 - acc: 0.2200 - ETA: 47s - loss: 2.8866 - acc: 0.2083 - ETA: 47s - loss: 2.8426 - acc: 0.2143 - ETA: 47s - loss: 3.0117 - acc: 0.1875 - ETA: 47s - loss: 3.1751 - acc: 0.1778 - ETA: 46s - loss: 3.1710 - acc: 0.1700 - ETA: 46s - loss: 3.1443 - acc: 0.1773 - ETA: 46s - loss: 3.1436 - acc: 0.1958 - ETA: 46s - loss: 3.1728 - acc: 0.1962 - ETA: 46s - loss: 3.1862 - acc: 0.1893 - ETA: 45s - loss: 3.1643 - acc: 0.1900 - ETA: 45s - loss: 3.1356 - acc: 0.1875 - ETA: 45s - loss: 3.1006 - acc: 0.1971 - ETA: 45s - loss: 3.0742 - acc: 0.2028 - ETA: 44s - loss: 3.0717 - acc: 0.2079 - ETA: 44s - loss: 3.0739 - acc: 0.2075 - ETA: 44s - loss: 3.1166 - acc: 0.2048 - ETA: 44s - loss: 3.1195 - acc: 0.2000 - ETA: 44s - loss: 3.1128 - acc: 0.2022 - ETA: 44s - loss: 3.1035 - acc: 0.2042 - 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ETA: 29s - loss: 3.1525 - acc: 0.2012 - ETA: 29s - loss: 3.1520 - acc: 0.2008 - ETA: 29s - loss: 3.1496 - acc: 0.2008 - ETA: 29s - loss: 3.1499 - acc: 0.2008 - ETA: 29s - loss: 3.1556 - acc: 0.2000 - ETA: 29s - loss: 3.1496 - acc: 0.2015 - ETA: 29s - loss: 3.1478 - acc: 0.2015 - ETA: 28s - loss: 3.1555 - acc: 0.2011 - ETA: 28s - loss: 3.1544 - acc: 0.2000 - ETA: 28s - loss: 3.1542 - acc: 0.2004 - ETA: 28s - loss: 3.1536 - acc: 0.2004 - ETA: 28s - loss: 3.1506 - acc: 0.2007 - ETA: 28s - loss: 3.1496 - acc: 0.2004 - ETA: 28s - loss: 3.1481 - acc: 0.2007 - ETA: 27s - loss: 3.1467 - acc: 0.2014 - ETA: 27s - loss: 3.1510 - acc: 0.2004 - ETA: 27s - loss: 3.1508 - acc: 0.2011 - ETA: 27s - loss: 3.1543 - acc: 0.2007 - ETA: 27s - loss: 3.1552 - acc: 0.2007 - ETA: 27s - loss: 3.1570 - acc: 0.2000 - ETA: 27s - loss: 3.1569 - acc: 0.2000 - ETA: 26s - loss: 3.1534 - acc: 0.2003 - ETA: 26s - loss: 3.1598 - acc: 0.1997 - ETA: 26s - loss: 3.1576 - acc: 0.2007 - ETA: 26s - loss: 3.1581 - acc: 0.2007 - ETA: 26s - loss: 3.1536 - acc: 0.2017 - ETA: 26s - loss: 3.1504 - acc: 0.2020 - ETA: 26s - loss: 3.1492 - acc: 0.2020 - ETA: 25s - loss: 3.1478 - acc: 0.2023 - ETA: 25s - loss: 3.1507 - acc: 0.2019 - ETA: 25s - loss: 3.1501 - acc: 0.2016 - ETA: 25s - loss: 3.1476 - acc: 0.2022 - ETA: 25s - loss: 3.1509 - acc: 0.2016 - ETA: 25s - loss: 3.1512 - acc: 0.2009 - ETA: 25s - loss: 3.1530 - acc: 0.2009 - ETA: 24s - loss: 3.1522 - acc: 0.2013 - ETA: 24s - loss: 3.1530 - acc: 0.2012 - ETA: 24s - loss: 3.1485 - acc: 0.2022 - ETA: 24s - loss: 3.1458 - acc: 0.2021 - ETA: 24s - loss: 3.1464 - acc: 0.2015 - ETA: 24s - loss: 3.1495 - acc: 0.2012 - ETA: 24s - loss: 3.1486 - acc: 0.2021 - ETA: 23s - loss: 3.1487 - acc: 0.2021 - ETA: 23s - loss: 3.1469 - acc: 0.2024 - ETA: 23s - loss: 3.1442 - acc: 0.2027 - ETA: 23s - loss: 3.1434 - acc: 0.2018 - ETA: 23s - loss: 3.1407 - acc: 0.2020 - ETA: 23s - loss: 3.1372 - acc: 0.2029 - ETA: 23s - loss: 3.1376 - acc: 0.2029 - ETA: 22s - loss: 3.1391 - acc: 0.2023 - ETA: 22s - loss: 3.1406 - acc: 0.2026 - ETA: 22s - loss: 3.1384 - acc: 0.2023 - ETA: 22s - loss: 3.1412 - acc: 0.2020 - ETA: 22s - loss: 3.1399 - acc: 0.2020 - ETA: 22s - loss: 3.1406 - acc: 0.2025 - ETA: 22s - loss: 3.1424 - acc: 0.2028 - ETA: 21s - loss: 3.1422 - acc: 0.2019 - ETA: 21s - loss: 3.1408 - acc: 0.2019 - ETA: 21s - loss: 3.1363 - acc: 0.2019 - ETA: 21s - loss: 3.1354 - acc: 0.2022 - ETA: 21s - loss: 3.1337 - acc: 0.2024 - ETA: 21s - loss: 3.1319 - acc: 0.2030 - ETA: 21s - loss: 3.1297 - acc: 0.2037 - ETA: 20s - loss: 3.1298 - acc: 0.2035 - ETA: 20s - loss: 3.1323 - acc: 0.2034 - ETA: 20s - loss: 3.1295 - acc: 0.2037 - ETA: 20s - loss: 3.1289 - acc: 0.2037 - ETA: 20s - loss: 3.1266 - acc: 0.2049 - ETA: 20s - loss: 3.1242 - acc: 0.2049 - ETA: 20s - loss: 3.1242 - acc: 0.2046 - ETA: 19s - loss: 3.1245 - acc: 0.2046 - ETA: 19s - loss: 3.1219 - acc: 0.2054 - ETA: 19s - loss: 3.1181 - acc: 0.2063 - ETA: 19s - loss: 3.1182 - acc: 0.2063 - ETA: 19s - loss: 3.1140 - acc: 0.2070 - 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ETA: 12s - loss: 3.1237 - acc: 0.2046 - ETA: 11s - loss: 3.1240 - acc: 0.2044 - ETA: 11s - loss: 3.1246 - acc: 0.2042 - ETA: 11s - loss: 3.1259 - acc: 0.2042 - ETA: 11s - loss: 3.1254 - acc: 0.2039 - ETA: 11s - loss: 3.1280 - acc: 0.2031 - ETA: 11s - loss: 3.1275 - acc: 0.2031 - ETA: 11s - loss: 3.1264 - acc: 0.2023 - ETA: 10s - loss: 3.1277 - acc: 0.2017 - ETA: 10s - loss: 3.1292 - acc: 0.2019 - ETA: 10s - loss: 3.1296 - acc: 0.2017 - ETA: 10s - loss: 3.1303 - acc: 0.2015 - ETA: 10s - loss: 3.1320 - acc: 0.2010 - ETA: 10s - loss: 3.1326 - acc: 0.2010 - ETA: 10s - loss: 3.1319 - acc: 0.2009 - ETA: 9s - loss: 3.1297 - acc: 0.2013  - ETA: 9s - loss: 3.1296 - acc: 0.2015 - ETA: 9s - loss: 3.1287 - acc: 0.2011 - ETA: 9s - loss: 3.1272 - acc: 0.2015 - ETA: 9s - loss: 3.1290 - acc: 0.2013 - ETA: 9s - loss: 3.1285 - acc: 0.2013 - ETA: 9s - loss: 3.1301 - acc: 0.2007 - ETA: 8s - loss: 3.1294 - acc: 0.2011 - ETA: 8s - loss: 3.1283 - acc: 0.2011 - ETA: 8s - loss: 3.1281 - acc: 0.2013 - ETA: 8s - loss: 3.1300 - acc: 0.2009 - ETA: 8s - loss: 3.1282 - acc: 0.2013 - ETA: 8s - loss: 3.1294 - acc: 0.2014 - ETA: 8s - loss: 3.1292 - acc: 0.2016 - ETA: 7s - loss: 3.1300 - acc: 0.2013 - ETA: 7s - loss: 3.1297 - acc: 0.2016 - ETA: 7s - loss: 3.1313 - acc: 0.2011 - ETA: 7s - loss: 3.1311 - acc: 0.2009 - ETA: 7s - loss: 3.1317 - acc: 0.2005 - ETA: 7s - loss: 3.1324 - acc: 0.2011 - ETA: 7s - loss: 3.1330 - acc: 0.2007 - ETA: 6s - loss: 3.1319 - acc: 0.2009 - ETA: 6s - loss: 3.1324 - acc: 0.2010 - ETA: 6s - loss: 3.1320 - acc: 0.2009 - ETA: 6s - loss: 3.1304 - acc: 0.2010 - ETA: 6s - loss: 3.1316 - acc: 0.2010 - ETA: 6s - loss: 3.1317 - acc: 0.2012 - ETA: 6s - loss: 3.1319 - acc: 0.2012 - ETA: 5s - loss: 3.1349 - acc: 0.2009 - ETA: 5s - loss: 3.1315 - acc: 0.2017 - ETA: 5s - loss: 3.1298 - acc: 0.2022 - ETA: 5s - loss: 3.1294 - acc: 0.2025 - ETA: 5s - loss: 3.1297 - acc: 0.2027 - ETA: 5s - loss: 3.1327 - acc: 0.2025 - ETA: 5s - loss: 3.1318 - acc: 0.2025 - ETA: 4s - loss: 3.1312 - acc: 0.2027 - ETA: 4s - loss: 3.1309 - acc: 0.2027 - ETA: 4s - loss: 3.1328 - acc: 0.2025 - ETA: 4s - loss: 3.1330 - acc: 0.2021 - ETA: 4s - loss: 3.1316 - acc: 0.2025 - ETA: 4s - loss: 3.1318 - acc: 0.2025 - ETA: 4s - loss: 3.1332 - acc: 0.2025 - ETA: 3s - loss: 3.1335 - acc: 0.2026 - ETA: 3s - loss: 3.1334 - acc: 0.2026 - ETA: 3s - loss: 3.1337 - acc: 0.2029 - ETA: 3s - loss: 3.1341 - acc: 0.2029 - ETA: 3s - loss: 3.1355 - acc: 0.2027 - ETA: 3s - loss: 3.1338 - acc: 0.2030 - ETA: 3s - loss: 3.1328 - acc: 0.2037 - ETA: 2s - loss: 3.1329 - acc: 0.2035 - ETA: 2s - loss: 3.1320 - acc: 0.2037 - ETA: 2s - loss: 3.1344 - acc: 0.2035 - ETA: 2s - loss: 3.1350 - acc: 0.2035 - ETA: 2s - loss: 3.1362 - acc: 0.2033 - ETA: 2s - loss: 3.1364 - acc: 0.2034 - ETA: 2s - loss: 3.1369 - acc: 0.2033 - ETA: 1s - loss: 3.1380 - acc: 0.2033 - ETA: 1s - loss: 3.1409 - acc: 0.2030 - ETA: 1s - loss: 3.1394 - acc: 0.2033 - ETA: 1s - loss: 3.1383 - acc: 0.2034 - ETA: 1s - loss: 3.1381 - acc: 0.2034 - ETA: 1s - loss: 3.1380 - acc: 0.2031 - ETA: 1s - loss: 3.1393 - acc: 0.2031 - ETA: 0s - loss: 3.1394 - acc: 0.2027 - ETA: 0s - loss: 3.1377 - acc: 0.2033 - ETA: 0s - loss: 3.1405 - acc: 0.2032 - ETA: 0s - loss: 3.1412 - acc: 0.2032 - ETA: 0s - loss: 3.1416 - acc: 0.2030 - ETA: 0s - loss: 3.1405 - acc: 0.2032Epoch 00039: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 3.1401 - acc: 0.2034 - val_loss: 3.5230 - val_acc: 0.1760
Out[17]:
<keras.callbacks.History at 0x1905da7b710>

Load the Model with the Best Validation Loss

In [18]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [19]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 20.0957%

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

In [20]:
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [21]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229.0
Trainable params: 68,229.0
Non-trainable params: 0.0
_________________________________________________________________

Compile the Model

In [22]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [23]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================>.] - ETA: 122s - loss: 15.6204 - acc: 0.0000e+00 - ETA: 15s - loss: 15.0209 - acc: 0.0056      - ETA: 11s - loss: 15.1737 - acc: 0.0038 - ETA: 9s - loss: 15.1404 - acc: 0.0056  - ETA: 8s - loss: 15.1128 - acc: 0.0068 - ETA: 7s - loss: 15.0954 - acc: 0.0054 - ETA: 6s - loss: 14.9845 - acc: 0.0109 - ETA: 5s - loss: 14.9023 - acc: 0.0128 - ETA: 5s - loss: 14.9227 - acc: 0.0131 - ETA: 5s - loss: 14.8798 - acc: 0.0152 - ETA: 5s - loss: 14.8799 - acc: 0.0160 - ETA: 5s - loss: 14.9067 - acc: 0.0151 - ETA: 5s - loss: 14.8875 - acc: 0.0149 - ETA: 4s - loss: 14.8181 - acc: 0.0183 - ETA: 4s - loss: 14.7155 - acc: 0.0232 - ETA: 4s - loss: 14.7233 - acc: 0.0229 - ETA: 4s - loss: 14.6735 - acc: 0.0253 - ETA: 4s - loss: 14.6399 - acc: 0.0253 - ETA: 4s - loss: 14.5698 - acc: 0.0273 - ETA: 3s - loss: 14.5577 - acc: 0.0285 - ETA: 3s - loss: 14.5421 - acc: 0.0289 - ETA: 3s - loss: 14.5019 - acc: 0.0291 - ETA: 3s - loss: 14.4281 - acc: 0.0301 - ETA: 3s - loss: 14.3774 - acc: 0.0335 - ETA: 3s - loss: 14.3153 - acc: 0.0338 - ETA: 3s - loss: 14.2827 - acc: 0.0361 - ETA: 3s - loss: 14.2393 - acc: 0.0391 - ETA: 3s - loss: 14.1901 - acc: 0.0409 - ETA: 3s - loss: 14.1235 - acc: 0.0439 - ETA: 3s - loss: 14.1064 - acc: 0.0449 - ETA: 3s - loss: 14.0865 - acc: 0.0446 - ETA: 2s - loss: 14.0674 - acc: 0.0458 - ETA: 2s - loss: 14.0129 - acc: 0.0476 - ETA: 2s - loss: 13.9615 - acc: 0.0500 - ETA: 2s - loss: 13.9198 - acc: 0.0525 - ETA: 2s - loss: 13.8995 - acc: 0.0534 - ETA: 2s - loss: 13.8396 - acc: 0.0545 - ETA: 2s - loss: 13.8209 - acc: 0.0557 - ETA: 2s - loss: 13.7670 - acc: 0.0575 - ETA: 2s - loss: 13.7296 - acc: 0.0579 - ETA: 2s - loss: 13.6878 - acc: 0.0595 - ETA: 2s - loss: 13.6328 - acc: 0.0608 - ETA: 2s - loss: 13.6150 - acc: 0.0612 - ETA: 2s - loss: 13.5529 - acc: 0.0629 - ETA: 1s - loss: 13.5187 - acc: 0.0636 - ETA: 1s - loss: 13.4704 - acc: 0.0650 - ETA: 1s - loss: 13.4378 - acc: 0.0670 - ETA: 1s - loss: 13.4091 - acc: 0.0683 - ETA: 1s - loss: 13.3589 - acc: 0.0705 - ETA: 1s - loss: 13.3271 - acc: 0.0722 - ETA: 1s - loss: 13.3048 - acc: 0.0729 - ETA: 1s - loss: 13.2783 - acc: 0.0745 - ETA: 1s - loss: 13.2379 - acc: 0.0766 - ETA: 1s - loss: 13.1842 - acc: 0.0791 - ETA: 1s - loss: 13.1551 - acc: 0.0808 - ETA: 1s - loss: 13.1120 - acc: 0.0831 - ETA: 1s - loss: 13.0715 - acc: 0.0854 - ETA: 1s - loss: 13.0154 - acc: 0.0890 - ETA: 0s - loss: 12.9798 - acc: 0.0907 - ETA: 0s - loss: 12.9439 - acc: 0.0914 - ETA: 0s - loss: 12.9093 - acc: 0.0928 - ETA: 0s - loss: 12.8826 - acc: 0.0936 - ETA: 0s - loss: 12.8295 - acc: 0.0962 - ETA: 0s - loss: 12.7942 - acc: 0.0968 - ETA: 0s - loss: 12.7625 - acc: 0.0982 - ETA: 0s - loss: 12.7293 - acc: 0.0995 - ETA: 0s - loss: 12.7017 - acc: 0.1006 - ETA: 0s - loss: 12.6931 - acc: 0.1010 - ETA: 0s - loss: 12.6683 - acc: 0.1022 - ETA: 0s - loss: 12.6239 - acc: 0.1044 - ETA: 0s - loss: 12.5953 - acc: 0.1052 - ETA: 0s - loss: 12.5533 - acc: 0.1065Epoch 00000: val_loss improved from inf to 10.63390, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 12.5085 - acc: 0.1088 - val_loss: 10.6339 - val_acc: 0.2036
Epoch 2/20
6560/6680 [============================>.] - ETA: 10s - loss: 7.9988 - acc: 0.4000 - ETA: 8s - loss: 10.1964 - acc: 0.2625 - ETA: 6s - loss: 10.1983 - acc: 0.2500 - ETA: 4s - loss: 10.1913 - acc: 0.2714 - ETA: 4s - loss: 10.3816 - acc: 0.2605 - ETA: 4s - loss: 10.5189 - acc: 0.2560 - ETA: 3s - loss: 10.4284 - acc: 0.2677 - ETA: 3s - loss: 10.3324 - acc: 0.2679 - ETA: 3s - loss: 10.4298 - acc: 0.2685 - ETA: 3s - loss: 10.3806 - acc: 0.2686 - ETA: 2s - loss: 10.4098 - acc: 0.2638 - ETA: 3s - loss: 10.4512 - acc: 0.2608 - ETA: 3s - loss: 10.3632 - acc: 0.2646 - ETA: 3s - loss: 10.3665 - acc: 0.2638 - ETA: 2s - loss: 10.3923 - acc: 0.2612 - ETA: 2s - loss: 10.3618 - acc: 0.2639 - ETA: 3s - loss: 10.3950 - acc: 0.2623 - ETA: 2s - loss: 10.3548 - acc: 0.2674 - ETA: 3s - loss: 10.3818 - acc: 0.2670 - ETA: 2s - loss: 10.3378 - acc: 0.2661 - ETA: 3s - loss: 10.2622 - acc: 0.2706 - ETA: 2s - loss: 10.2732 - acc: 0.2686 - ETA: 2s - loss: 10.2788 - acc: 0.2670 - ETA: 2s - loss: 10.1980 - acc: 0.2703 - ETA: 2s - loss: 10.1745 - acc: 0.2726 - ETA: 2s - loss: 10.1959 - acc: 0.2688 - ETA: 2s - loss: 10.1772 - acc: 0.2697 - ETA: 2s - loss: 10.1867 - acc: 0.2697 - ETA: 2s - loss: 10.1685 - acc: 0.2717 - ETA: 2s - loss: 10.1595 - acc: 0.2723 - ETA: 2s - loss: 10.1349 - acc: 0.2735 - ETA: 2s - loss: 10.1103 - acc: 0.2747 - ETA: 2s - loss: 10.0920 - acc: 0.2768 - ETA: 2s - loss: 10.1342 - acc: 0.2755 - ETA: 2s - loss: 10.1230 - acc: 0.2764 - ETA: 2s - loss: 10.1278 - acc: 0.2767 - ETA: 2s - loss: 10.1253 - acc: 0.2768 - ETA: 2s - loss: 10.1576 - acc: 0.2744 - ETA: 2s - loss: 10.1430 - acc: 0.2746 - ETA: 2s - loss: 10.1538 - acc: 0.2739 - ETA: 2s - loss: 10.1672 - acc: 0.2736 - ETA: 2s - loss: 10.1502 - acc: 0.2751 - ETA: 1s - loss: 10.1637 - acc: 0.2733 - ETA: 1s - loss: 10.1557 - acc: 0.2733 - ETA: 1s - loss: 10.1430 - acc: 0.2737 - ETA: 1s - loss: 10.1345 - acc: 0.2751 - ETA: 1s - loss: 10.0985 - acc: 0.2772 - ETA: 1s - loss: 10.0666 - acc: 0.2784 - ETA: 1s - loss: 10.0524 - acc: 0.2804 - ETA: 1s - loss: 10.0200 - acc: 0.2817 - ETA: 1s - loss: 10.0100 - acc: 0.2828 - ETA: 1s - loss: 10.0082 - acc: 0.2843 - ETA: 1s - loss: 10.0118 - acc: 0.2838 - ETA: 1s - loss: 10.0133 - acc: 0.2837 - ETA: 1s - loss: 10.0320 - acc: 0.2827 - ETA: 1s - loss: 10.0357 - acc: 0.2827 - ETA: 1s - loss: 10.0465 - acc: 0.2825 - ETA: 1s - loss: 10.0185 - acc: 0.2836 - ETA: 0s - loss: 10.0124 - acc: 0.2838 - ETA: 0s - loss: 9.9944 - acc: 0.2851  - ETA: 0s - loss: 9.9843 - acc: 0.2864 - ETA: 0s - loss: 9.9851 - acc: 0.2863 - ETA: 0s - loss: 9.9975 - acc: 0.2855 - ETA: 0s - loss: 9.9961 - acc: 0.2860 - ETA: 0s - loss: 9.9887 - acc: 0.2871 - ETA: 0s - loss: 9.9909 - acc: 0.2863 - ETA: 0s - loss: 9.9851 - acc: 0.2856 - ETA: 0s - loss: 9.9956 - acc: 0.2857 - ETA: 0s - loss: 9.9643 - acc: 0.2875 - ETA: 0s - loss: 9.9649 - acc: 0.2875 - ETA: 0s - loss: 9.9545 - acc: 0.2884 - ETA: 0s - loss: 9.9668 - acc: 0.2877 - ETA: 0s - loss: 9.9747 - acc: 0.2874 - ETA: 0s - loss: 9.9728 - acc: 0.2875 - ETA: 0s - loss: 9.9290 - acc: 0.2909Epoch 00001: val_loss improved from 10.63390 to 9.78852, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 9.9386 - acc: 0.2907 - val_loss: 9.7885 - val_acc: 0.2958
Epoch 3/20
6640/6680 [============================>.] - ETA: 5s - loss: 11.3728 - acc: 0.2500 - ETA: 3s - loss: 9.5522 - acc: 0.3417  - ETA: 3s - loss: 9.6589 - acc: 0.3250 - ETA: 3s - loss: 9.5376 - acc: 0.3267 - ETA: 3s - loss: 9.5762 - acc: 0.3289 - ETA: 4s - loss: 9.3523 - acc: 0.3409 - ETA: 4s - loss: 9.2299 - acc: 0.3500 - ETA: 4s - loss: 9.1919 - acc: 0.3550 - ETA: 4s - loss: 9.3147 - acc: 0.3515 - ETA: 4s - loss: 9.2549 - acc: 0.3539 - ETA: 3s - loss: 9.2721 - acc: 0.3522 - ETA: 4s - loss: 9.1631 - acc: 0.3594 - ETA: 4s - loss: 9.0353 - acc: 0.3657 - ETA: 3s - loss: 8.9675 - acc: 0.3723 - ETA: 3s - loss: 9.0646 - acc: 0.3648 - ETA: 3s - loss: 9.0288 - acc: 0.3672 - ETA: 3s - loss: 9.0819 - acc: 0.3627 - ETA: 3s - loss: 9.0070 - acc: 0.3671 - ETA: 3s - loss: 9.1066 - acc: 0.3606 - ETA: 3s - loss: 9.0887 - acc: 0.3616 - ETA: 3s - loss: 9.1567 - acc: 0.3580 - ETA: 3s - loss: 9.0457 - acc: 0.3658 - ETA: 3s - loss: 9.0742 - acc: 0.3647 - ETA: 3s - loss: 9.1388 - acc: 0.3630 - ETA: 3s - loss: 9.1668 - acc: 0.3615 - ETA: 3s - loss: 9.1915 - acc: 0.3610 - ETA: 3s - loss: 9.1912 - acc: 0.3611 - ETA: 2s - loss: 9.2462 - acc: 0.3585 - ETA: 2s - loss: 9.2469 - acc: 0.3583 - ETA: 2s - loss: 9.2306 - acc: 0.3588 - ETA: 2s - loss: 9.2280 - acc: 0.3588 - ETA: 2s - loss: 9.2359 - acc: 0.3572 - ETA: 2s - loss: 9.2526 - acc: 0.3570 - ETA: 2s - loss: 9.2529 - acc: 0.3567 - ETA: 2s - loss: 9.2579 - acc: 0.3548 - ETA: 2s - loss: 9.2424 - acc: 0.3553 - ETA: 2s - loss: 9.1902 - acc: 0.3596 - ETA: 2s - loss: 9.2259 - acc: 0.3556 - ETA: 2s - loss: 9.2176 - acc: 0.3554 - ETA: 2s - loss: 9.2247 - acc: 0.3559 - ETA: 2s - loss: 9.1811 - acc: 0.3597 - ETA: 2s - loss: 9.2049 - acc: 0.3587 - ETA: 2s - loss: 9.2182 - acc: 0.3571 - ETA: 1s - loss: 9.2471 - acc: 0.3550 - ETA: 1s - loss: 9.2641 - acc: 0.3546 - ETA: 1s - loss: 9.2693 - acc: 0.3543 - ETA: 1s - loss: 9.2676 - acc: 0.3549 - ETA: 1s - loss: 9.2691 - acc: 0.3554 - ETA: 1s - loss: 9.2875 - acc: 0.3545 - ETA: 1s - loss: 9.2824 - acc: 0.3558 - ETA: 1s - loss: 9.2761 - acc: 0.3558 - ETA: 1s - loss: 9.2660 - acc: 0.3568 - ETA: 1s - loss: 9.2690 - acc: 0.3567 - ETA: 1s - loss: 9.2739 - acc: 0.3561 - ETA: 1s - loss: 9.3030 - acc: 0.3542 - ETA: 1s - loss: 9.2951 - acc: 0.3552 - ETA: 1s - loss: 9.3163 - acc: 0.3543 - ETA: 1s - loss: 9.2933 - acc: 0.3562 - ETA: 1s - loss: 9.3263 - acc: 0.3541 - ETA: 1s - loss: 9.3458 - acc: 0.3525 - ETA: 0s - loss: 9.3573 - acc: 0.3515 - ETA: 0s - loss: 9.3867 - acc: 0.3494 - ETA: 0s - loss: 9.3840 - acc: 0.3493 - ETA: 0s - loss: 9.3886 - acc: 0.3493 - ETA: 0s - loss: 9.3922 - acc: 0.3493 - ETA: 0s - loss: 9.4048 - acc: 0.3484 - ETA: 0s - loss: 9.4004 - acc: 0.3491 - ETA: 0s - loss: 9.4042 - acc: 0.3492 - ETA: 0s - loss: 9.4237 - acc: 0.3477 - ETA: 0s - loss: 9.4131 - acc: 0.3483 - ETA: 0s - loss: 9.4328 - acc: 0.3474 - ETA: 0s - loss: 9.4329 - acc: 0.3473 - ETA: 0s - loss: 9.4218 - acc: 0.3481 - ETA: 0s - loss: 9.4304 - acc: 0.3470 - ETA: 0s - loss: 9.4341 - acc: 0.3469 - ETA: 0s - loss: 9.4393 - acc: 0.3463 - ETA: 0s - loss: 9.4367 - acc: 0.3471Epoch 00002: val_loss improved from 9.78852 to 9.51859, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 9.4312 - acc: 0.3479 - val_loss: 9.5186 - val_acc: 0.3413
Epoch 4/20
6600/6680 [============================>.] - ETA: 10s - loss: 7.3327 - acc: 0.5000 - ETA: 3s - loss: 8.6937 - acc: 0.4286  - ETA: 3s - loss: 8.5837 - acc: 0.4357 - ETA: 3s - loss: 8.5468 - acc: 0.4289 - ETA: 3s - loss: 8.2928 - acc: 0.4455 - ETA: 3s - loss: 8.2641 - acc: 0.4442 - ETA: 3s - loss: 8.2789 - acc: 0.4383 - ETA: 3s - loss: 8.4450 - acc: 0.4278 - ETA: 3s - loss: 8.6051 - acc: 0.4188 - ETA: 3s - loss: 8.6031 - acc: 0.4167 - ETA: 3s - loss: 8.6600 - acc: 0.4146 - ETA: 3s - loss: 8.6419 - acc: 0.4183 - ETA: 3s - loss: 8.6690 - acc: 0.4167 - ETA: 3s - loss: 8.6591 - acc: 0.4164 - ETA: 3s - loss: 8.7583 - acc: 0.4102 - ETA: 3s - loss: 8.7871 - acc: 0.4087 - ETA: 3s - loss: 8.8388 - acc: 0.4054 - ETA: 3s - loss: 8.9042 - acc: 0.4013 - ETA: 3s - loss: 8.8628 - acc: 0.4042 - ETA: 3s - loss: 8.9325 - acc: 0.4017 - ETA: 3s - loss: 8.9114 - acc: 0.4038 - ETA: 3s - loss: 8.9529 - acc: 0.4021 - ETA: 3s - loss: 9.0203 - acc: 0.3965 - ETA: 3s - loss: 9.0080 - acc: 0.3966 - ETA: 3s - loss: 9.0296 - acc: 0.3948 - ETA: 3s - loss: 9.0310 - acc: 0.3945 - ETA: 3s - loss: 9.0718 - acc: 0.3920 - ETA: 3s - loss: 9.0130 - acc: 0.3957 - ETA: 3s - loss: 9.0068 - acc: 0.3967 - ETA: 2s - loss: 8.9964 - acc: 0.3980 - ETA: 2s - loss: 8.9800 - acc: 0.3977 - ETA: 2s - loss: 8.9740 - acc: 0.3978 - ETA: 2s - loss: 8.9698 - acc: 0.3976 - ETA: 2s - loss: 9.0109 - acc: 0.3963 - ETA: 2s - loss: 9.0250 - acc: 0.3961 - ETA: 2s - loss: 9.0833 - acc: 0.3933 - ETA: 2s - loss: 9.0996 - acc: 0.3932 - ETA: 2s - loss: 9.1089 - acc: 0.3925 - ETA: 2s - loss: 9.1353 - acc: 0.3901 - ETA: 2s - loss: 9.0918 - acc: 0.3917 - ETA: 2s - loss: 9.0797 - acc: 0.3919 - ETA: 2s - loss: 9.0789 - acc: 0.3923 - ETA: 2s - loss: 9.0880 - acc: 0.3920 - ETA: 2s - loss: 9.0963 - acc: 0.3913 - ETA: 1s - loss: 9.1112 - acc: 0.3906 - ETA: 1s - loss: 9.1168 - acc: 0.3896 - ETA: 1s - loss: 9.0905 - acc: 0.3919 - ETA: 1s - loss: 9.1279 - acc: 0.3899 - ETA: 1s - loss: 9.1573 - acc: 0.3883 - ETA: 1s - loss: 9.1631 - acc: 0.3880 - ETA: 1s - loss: 9.1695 - acc: 0.3882 - ETA: 1s - loss: 9.1779 - acc: 0.3880 - ETA: 1s - loss: 9.1565 - acc: 0.3888 - ETA: 1s - loss: 9.1439 - acc: 0.3890 - ETA: 1s - loss: 9.1618 - acc: 0.3883 - ETA: 1s - loss: 9.1803 - acc: 0.3870 - ETA: 1s - loss: 9.1754 - acc: 0.3866 - ETA: 1s - loss: 9.1899 - acc: 0.3855 - ETA: 1s - loss: 9.1737 - acc: 0.3863 - ETA: 0s - loss: 9.1724 - acc: 0.3861 - ETA: 0s - loss: 9.1997 - acc: 0.3841 - ETA: 0s - loss: 9.1996 - acc: 0.3840 - ETA: 0s - loss: 9.2069 - acc: 0.3835 - ETA: 0s - loss: 9.2196 - acc: 0.3825 - ETA: 0s - loss: 9.1955 - acc: 0.3840 - ETA: 0s - loss: 9.2003 - acc: 0.3841 - ETA: 0s - loss: 9.2074 - acc: 0.3838 - ETA: 0s - loss: 9.1921 - acc: 0.3836 - ETA: 0s - loss: 9.1954 - acc: 0.3836 - ETA: 0s - loss: 9.1946 - acc: 0.3833 - ETA: 0s - loss: 9.2080 - acc: 0.3825 - ETA: 0s - loss: 9.2221 - acc: 0.3816 - ETA: 0s - loss: 9.2480 - acc: 0.3800Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 9.2532 - acc: 0.3802 - val_loss: 9.5532 - val_acc: 0.3329
Epoch 5/20
6520/6680 [============================>.] - ETA: 10s - loss: 7.3671 - acc: 0.5000 - ETA: 5s - loss: 9.2686 - acc: 0.4000  - ETA: 4s - loss: 9.7318 - acc: 0.3682 - ETA: 3s - loss: 9.4110 - acc: 0.3912 - ETA: 3s - loss: 9.5194 - acc: 0.3841 - ETA: 3s - loss: 9.3640 - acc: 0.3920 - ETA: 3s - loss: 9.5238 - acc: 0.3767 - ETA: 3s - loss: 9.5854 - acc: 0.3742 - ETA: 3s - loss: 9.4655 - acc: 0.3833 - ETA: 3s - loss: 9.3827 - acc: 0.3864 - ETA: 3s - loss: 9.3661 - acc: 0.3850 - ETA: 3s - loss: 9.3625 - acc: 0.3849 - ETA: 4s - loss: 9.4336 - acc: 0.3809 - ETA: 4s - loss: 9.3485 - acc: 0.3851 - ETA: 3s - loss: 9.3148 - acc: 0.3879 - ETA: 3s - loss: 9.2307 - acc: 0.3919 - ETA: 3s - loss: 9.2261 - acc: 0.3924 - ETA: 3s - loss: 9.2709 - acc: 0.3888 - ETA: 3s - loss: 9.3802 - acc: 0.3833 - ETA: 3s - loss: 9.3791 - acc: 0.3833 - ETA: 3s - loss: 9.3069 - acc: 0.3860 - ETA: 3s - loss: 9.3236 - acc: 0.3860 - ETA: 3s - loss: 9.3017 - acc: 0.3880 - ETA: 3s - loss: 9.2875 - acc: 0.3885 - ETA: 3s - loss: 9.2654 - acc: 0.3904 - ETA: 3s - loss: 9.2887 - acc: 0.3903 - ETA: 3s - loss: 9.2663 - acc: 0.3907 - ETA: 3s - loss: 9.2709 - acc: 0.3896 - ETA: 3s - loss: 9.2873 - acc: 0.3887 - ETA: 2s - loss: 9.2803 - acc: 0.3896 - ETA: 2s - loss: 9.3204 - acc: 0.3864 - ETA: 2s - loss: 9.3016 - acc: 0.3877 - ETA: 2s - loss: 9.3086 - acc: 0.3877 - ETA: 2s - loss: 9.2935 - acc: 0.3888 - ETA: 2s - loss: 9.2652 - acc: 0.3909 - ETA: 2s - loss: 9.2619 - acc: 0.3914 - ETA: 2s - loss: 9.2487 - acc: 0.3930 - ETA: 2s - loss: 9.2443 - acc: 0.3939 - ETA: 2s - loss: 9.2351 - acc: 0.3948 - ETA: 2s - loss: 9.2322 - acc: 0.3959 - ETA: 2s - loss: 9.2376 - acc: 0.3960 - ETA: 2s - loss: 9.2633 - acc: 0.3944 - ETA: 2s - loss: 9.2250 - acc: 0.3954 - ETA: 1s - loss: 9.2773 - acc: 0.3927 - ETA: 1s - loss: 9.2906 - acc: 0.3916 - ETA: 1s - loss: 9.2865 - acc: 0.3913 - ETA: 1s - loss: 9.2655 - acc: 0.3920 - ETA: 1s - loss: 9.2753 - acc: 0.3922 - ETA: 1s - loss: 9.2366 - acc: 0.3940 - ETA: 1s - loss: 9.2175 - acc: 0.3950 - ETA: 1s - loss: 9.2147 - acc: 0.3947 - ETA: 1s - loss: 9.2024 - acc: 0.3952 - ETA: 1s - loss: 9.1709 - acc: 0.3972 - ETA: 1s - loss: 9.1888 - acc: 0.3954 - ETA: 1s - loss: 9.1934 - acc: 0.3947 - ETA: 1s - loss: 9.2048 - acc: 0.3933 - ETA: 1s - loss: 9.1917 - acc: 0.3940 - ETA: 1s - loss: 9.1838 - acc: 0.3943 - ETA: 1s - loss: 9.1946 - acc: 0.3940 - ETA: 0s - loss: 9.1948 - acc: 0.3937 - ETA: 0s - loss: 9.2032 - acc: 0.3935 - ETA: 0s - loss: 9.1861 - acc: 0.3945 - ETA: 0s - loss: 9.1868 - acc: 0.3946 - ETA: 0s - loss: 9.1944 - acc: 0.3941 - ETA: 0s - loss: 9.1899 - acc: 0.3949 - ETA: 0s - loss: 9.1834 - acc: 0.3948 - ETA: 0s - loss: 9.1924 - acc: 0.3940 - ETA: 0s - loss: 9.1813 - acc: 0.3949 - ETA: 0s - loss: 9.1705 - acc: 0.3958 - ETA: 0s - loss: 9.1737 - acc: 0.3956 - ETA: 0s - loss: 9.1773 - acc: 0.3955 - ETA: 0s - loss: 9.1918 - acc: 0.3947 - ETA: 0s - loss: 9.1915 - acc: 0.3946 - ETA: 0s - loss: 9.1953 - acc: 0.3946 - ETA: 0s - loss: 9.1844 - acc: 0.3949Epoch 00004: val_loss improved from 9.51859 to 9.42262, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 9.1428 - acc: 0.3978 - val_loss: 9.4226 - val_acc: 0.3401
Epoch 6/20
6620/6680 [============================>.] - ETA: 0s - loss: 6.7605 - acc: 0.5500 - ETA: 4s - loss: 8.1808 - acc: 0.4750 - ETA: 4s - loss: 8.7315 - acc: 0.4500 - ETA: 4s - loss: 8.9461 - acc: 0.4375 - ETA: 4s - loss: 8.9452 - acc: 0.4375 - ETA: 4s - loss: 8.9499 - acc: 0.4333 - ETA: 4s - loss: 8.8875 - acc: 0.4346 - ETA: 4s - loss: 8.9778 - acc: 0.4283 - ETA: 4s - loss: 9.2547 - acc: 0.4088 - ETA: 4s - loss: 9.1568 - acc: 0.4125 - ETA: 3s - loss: 8.9452 - acc: 0.4228 - ETA: 3s - loss: 8.9410 - acc: 0.4186 - ETA: 3s - loss: 8.9696 - acc: 0.4161 - ETA: 3s - loss: 9.0879 - acc: 0.4102 - ETA: 3s - loss: 9.1217 - acc: 0.4063 - ETA: 3s - loss: 9.0848 - acc: 0.4096 - ETA: 3s - loss: 9.1661 - acc: 0.4049 - ETA: 3s - loss: 9.2036 - acc: 0.4020 - ETA: 3s - loss: 9.1971 - acc: 0.4031 - ETA: 3s - loss: 9.1476 - acc: 0.4066 - ETA: 3s - loss: 9.1593 - acc: 0.4064 - ETA: 3s - loss: 9.1636 - acc: 0.4051 - ETA: 3s - loss: 9.1132 - acc: 0.4070 - ETA: 3s - loss: 9.1294 - acc: 0.4067 - ETA: 3s - loss: 9.1150 - acc: 0.4070 - ETA: 3s - loss: 9.1279 - acc: 0.4062 - ETA: 3s - loss: 9.0861 - acc: 0.4092 - ETA: 3s - loss: 9.1305 - acc: 0.4061 - ETA: 3s - loss: 9.1559 - acc: 0.4047 - ETA: 3s - loss: 9.1478 - acc: 0.4049 - ETA: 2s - loss: 9.1061 - acc: 0.4071 - ETA: 2s - loss: 9.0902 - acc: 0.4081 - ETA: 2s - loss: 9.0369 - acc: 0.4119 - ETA: 2s - loss: 9.0040 - acc: 0.4136 - ETA: 2s - loss: 9.0356 - acc: 0.4115 - ETA: 2s - loss: 9.0459 - acc: 0.4094 - ETA: 2s - loss: 9.0924 - acc: 0.4067 - ETA: 2s - loss: 9.1325 - acc: 0.4039 - ETA: 2s - loss: 9.1416 - acc: 0.4036 - ETA: 2s - loss: 9.1634 - acc: 0.4022 - ETA: 2s - loss: 9.1688 - acc: 0.4019 - ETA: 2s - loss: 9.2034 - acc: 0.3997 - ETA: 2s - loss: 9.1839 - acc: 0.4014 - ETA: 2s - loss: 9.1775 - acc: 0.4020 - ETA: 2s - loss: 9.1451 - acc: 0.4031 - ETA: 2s - loss: 9.1180 - acc: 0.4044 - ETA: 2s - loss: 9.1529 - acc: 0.4024 - ETA: 2s - loss: 9.1015 - acc: 0.4049 - ETA: 2s - loss: 9.1078 - acc: 0.4046 - ETA: 1s - loss: 9.1121 - acc: 0.4050 - ETA: 1s - loss: 9.1353 - acc: 0.4039 - ETA: 1s - loss: 9.1070 - acc: 0.4048 - ETA: 1s - loss: 9.1097 - acc: 0.4049 - ETA: 1s - loss: 9.1135 - acc: 0.4045 - ETA: 1s - loss: 9.1463 - acc: 0.4022 - ETA: 1s - loss: 9.1512 - acc: 0.4022 - ETA: 1s - loss: 9.1226 - acc: 0.4032 - ETA: 1s - loss: 9.0991 - acc: 0.4042 - ETA: 1s - loss: 9.0790 - acc: 0.4056 - ETA: 1s - loss: 9.0882 - acc: 0.4057 - ETA: 1s - loss: 9.0887 - acc: 0.4058 - ETA: 1s - loss: 9.0843 - acc: 0.4060 - ETA: 1s - loss: 9.0717 - acc: 0.4073 - ETA: 1s - loss: 9.0634 - acc: 0.4075 - ETA: 0s - loss: 9.0626 - acc: 0.4076 - ETA: 0s - loss: 9.0777 - acc: 0.4067 - ETA: 0s - loss: 9.0775 - acc: 0.4069 - ETA: 0s - loss: 9.0875 - acc: 0.4063 - ETA: 0s - loss: 9.0608 - acc: 0.4082 - ETA: 0s - loss: 9.0774 - acc: 0.4073 - ETA: 0s - loss: 9.0426 - acc: 0.4099 - ETA: 0s - loss: 9.0309 - acc: 0.4106 - ETA: 0s - loss: 9.0132 - acc: 0.4113 - ETA: 0s - loss: 9.0028 - acc: 0.4118 - ETA: 0s - loss: 9.0091 - acc: 0.4116 - ETA: 0s - loss: 9.0029 - acc: 0.4121 - ETA: 0s - loss: 8.9803 - acc: 0.4134 - ETA: 0s - loss: 8.9806 - acc: 0.4131 - ETA: 0s - loss: 8.9913 - acc: 0.4125Epoch 00005: val_loss improved from 9.42262 to 9.40859, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.0008 - acc: 0.4117 - val_loss: 9.4086 - val_acc: 0.3533
Epoch 7/20
6620/6680 [============================>.] - ETA: 5s - loss: 8.1183 - acc: 0.4500 - ETA: 3s - loss: 8.8440 - acc: 0.4333 - ETA: 3s - loss: 8.2774 - acc: 0.4688 - ETA: 3s - loss: 8.5702 - acc: 0.4500 - ETA: 3s - loss: 8.6059 - acc: 0.4458 - ETA: 3s - loss: 8.6344 - acc: 0.4446 - ETA: 3s - loss: 8.6053 - acc: 0.4484 - ETA: 3s - loss: 8.7101 - acc: 0.4444 - ETA: 4s - loss: 8.6547 - acc: 0.4487 - ETA: 4s - loss: 8.7380 - acc: 0.4420 - ETA: 3s - loss: 8.8511 - acc: 0.4347 - ETA: 4s - loss: 8.9159 - acc: 0.4314 - ETA: 4s - loss: 8.8289 - acc: 0.4373 - ETA: 4s - loss: 8.8174 - acc: 0.4373 - ETA: 3s - loss: 8.8416 - acc: 0.4359 - ETA: 3s - loss: 8.8684 - acc: 0.4348 - ETA: 3s - loss: 8.8618 - acc: 0.4347 - ETA: 3s - loss: 8.8699 - acc: 0.4325 - ETA: 3s - loss: 8.8263 - acc: 0.4326 - ETA: 3s - loss: 8.7896 - acc: 0.4348 - ETA: 3s - loss: 8.7493 - acc: 0.4366 - ETA: 3s - loss: 8.7782 - acc: 0.4354 - ETA: 3s - loss: 8.7825 - acc: 0.4360 - ETA: 3s - loss: 8.7477 - acc: 0.4385 - ETA: 3s - loss: 8.7130 - acc: 0.4402 - ETA: 3s - loss: 8.7766 - acc: 0.4369 - ETA: 3s - loss: 8.7265 - acc: 0.4404 - ETA: 3s - loss: 8.7641 - acc: 0.4378 - ETA: 2s - loss: 8.7950 - acc: 0.4356 - ETA: 2s - loss: 8.8135 - acc: 0.4342 - ETA: 2s - loss: 8.8511 - acc: 0.4323 - ETA: 2s - loss: 8.8589 - acc: 0.4320 - ETA: 2s - loss: 8.9250 - acc: 0.4280 - ETA: 2s - loss: 8.9399 - acc: 0.4277 - ETA: 2s - loss: 8.9249 - acc: 0.4283 - ETA: 2s - loss: 8.9266 - acc: 0.4279 - ETA: 2s - loss: 8.9328 - acc: 0.4261 - ETA: 2s - loss: 8.9290 - acc: 0.4259 - ETA: 2s - loss: 8.9004 - acc: 0.4269 - ETA: 2s - loss: 8.8628 - acc: 0.4292 - ETA: 2s - loss: 8.8772 - acc: 0.4288 - ETA: 2s - loss: 8.8923 - acc: 0.4280 - ETA: 2s - loss: 8.8873 - acc: 0.4275 - ETA: 1s - loss: 8.8840 - acc: 0.4279 - ETA: 1s - loss: 8.8981 - acc: 0.4270 - ETA: 1s - loss: 8.9141 - acc: 0.4264 - ETA: 1s - loss: 8.9255 - acc: 0.4257 - ETA: 1s - loss: 8.9440 - acc: 0.4248 - ETA: 1s - loss: 8.9626 - acc: 0.4234 - ETA: 1s - loss: 8.9839 - acc: 0.4218 - ETA: 1s - loss: 8.9778 - acc: 0.4227 - ETA: 1s - loss: 8.9715 - acc: 0.4222 - ETA: 1s - loss: 8.9740 - acc: 0.4221 - ETA: 1s - loss: 8.9714 - acc: 0.4213 - ETA: 1s - loss: 8.9606 - acc: 0.4221 - ETA: 1s - loss: 8.9479 - acc: 0.4227 - ETA: 1s - loss: 8.9488 - acc: 0.4227 - ETA: 1s - loss: 8.9590 - acc: 0.4216 - ETA: 1s - loss: 8.9720 - acc: 0.4206 - ETA: 1s - loss: 8.9681 - acc: 0.4209 - ETA: 0s - loss: 8.9741 - acc: 0.4207 - ETA: 0s - loss: 8.9510 - acc: 0.4225 - ETA: 0s - loss: 8.9635 - acc: 0.4217 - ETA: 0s - loss: 8.9394 - acc: 0.4229 - ETA: 0s - loss: 8.9338 - acc: 0.4230 - ETA: 0s - loss: 8.9114 - acc: 0.4241 - ETA: 0s - loss: 8.8874 - acc: 0.4255 - ETA: 0s - loss: 8.8492 - acc: 0.4279 - ETA: 0s - loss: 8.8575 - acc: 0.4277 - ETA: 0s - loss: 8.8505 - acc: 0.4281 - ETA: 0s - loss: 8.8521 - acc: 0.4278 - ETA: 0s - loss: 8.8455 - acc: 0.4283 - ETA: 0s - loss: 8.8525 - acc: 0.4277 - ETA: 0s - loss: 8.8607 - acc: 0.4269Epoch 00006: val_loss improved from 9.40859 to 9.29745, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.8550 - acc: 0.4274 - val_loss: 9.2974 - val_acc: 0.3737
Epoch 8/20
6660/6680 [============================>.] - ETA: 7s - loss: 8.1181 - acc: 0.5000 - ETA: 5s - loss: 7.6519 - acc: 0.4900 - ETA: 4s - loss: 8.5516 - acc: 0.4500 - ETA: 4s - loss: 8.8655 - acc: 0.4267 - ETA: 3s - loss: 8.5296 - acc: 0.4525 - ETA: 3s - loss: 8.3656 - acc: 0.4615 - ETA: 3s - loss: 8.4486 - acc: 0.4567 - ETA: 3s - loss: 8.5325 - acc: 0.4543 - ETA: 3s - loss: 8.6355 - acc: 0.4487 - ETA: 3s - loss: 8.5252 - acc: 0.4548 - ETA: 3s - loss: 8.6214 - acc: 0.4489 - ETA: 3s - loss: 8.6205 - acc: 0.4469 - ETA: 3s - loss: 8.6753 - acc: 0.4434 - ETA: 3s - loss: 8.6436 - acc: 0.4458 - ETA: 3s - loss: 8.6634 - acc: 0.4453 - ETA: 3s - loss: 8.6869 - acc: 0.4433 - ETA: 3s - loss: 8.6736 - acc: 0.4443 - ETA: 3s - loss: 8.6759 - acc: 0.4446 - ETA: 3s - loss: 8.6605 - acc: 0.4449 - ETA: 3s - loss: 8.6830 - acc: 0.4446 - ETA: 3s - loss: 8.7043 - acc: 0.4438 - ETA: 3s - loss: 8.7210 - acc: 0.4429 - ETA: 3s - loss: 8.7342 - acc: 0.4426 - ETA: 3s - loss: 8.7586 - acc: 0.4409 - ETA: 3s - loss: 8.7942 - acc: 0.4393 - ETA: 3s - loss: 8.8547 - acc: 0.4355 - ETA: 2s - loss: 8.8632 - acc: 0.4357 - ETA: 2s - loss: 8.8834 - acc: 0.4339 - ETA: 2s - loss: 8.8357 - acc: 0.4374 - ETA: 2s - loss: 8.8386 - acc: 0.4368 - ETA: 2s - loss: 8.8529 - acc: 0.4359 - ETA: 2s - loss: 8.8301 - acc: 0.4379 - ETA: 2s - loss: 8.7986 - acc: 0.4392 - ETA: 2s - loss: 8.8204 - acc: 0.4381 - ETA: 2s - loss: 8.8270 - acc: 0.4378 - ETA: 2s - loss: 8.8355 - acc: 0.4373 - ETA: 2s - loss: 8.9033 - acc: 0.4333 - ETA: 2s - loss: 8.8978 - acc: 0.4340 - ETA: 2s - loss: 8.8894 - acc: 0.4344 - ETA: 2s - loss: 8.8766 - acc: 0.4351 - ETA: 2s - loss: 8.8864 - acc: 0.4347 - ETA: 2s - loss: 8.8705 - acc: 0.4356 - ETA: 2s - loss: 8.8588 - acc: 0.4366 - ETA: 1s - loss: 8.8873 - acc: 0.4346 - ETA: 1s - loss: 8.9158 - acc: 0.4331 - ETA: 1s - loss: 8.9067 - acc: 0.4340 - ETA: 1s - loss: 8.8996 - acc: 0.4342 - ETA: 1s - loss: 8.9108 - acc: 0.4338 - ETA: 1s - loss: 8.8945 - acc: 0.4352 - ETA: 1s - loss: 8.8769 - acc: 0.4362 - ETA: 1s - loss: 8.8696 - acc: 0.4364 - ETA: 1s - loss: 8.9053 - acc: 0.4343 - ETA: 1s - loss: 8.8977 - acc: 0.4345 - ETA: 1s - loss: 8.9011 - acc: 0.4344 - ETA: 1s - loss: 8.8983 - acc: 0.4343 - ETA: 1s - loss: 8.8882 - acc: 0.4353 - ETA: 1s - loss: 8.8790 - acc: 0.4357 - ETA: 1s - loss: 8.8611 - acc: 0.4364 - ETA: 0s - loss: 8.8287 - acc: 0.4383 - ETA: 0s - loss: 8.8293 - acc: 0.4384 - ETA: 0s - loss: 8.8310 - acc: 0.4383 - ETA: 0s - loss: 8.8273 - acc: 0.4382 - ETA: 0s - loss: 8.8432 - acc: 0.4373 - ETA: 0s - loss: 8.8468 - acc: 0.4369 - ETA: 0s - loss: 8.8448 - acc: 0.4371 - ETA: 0s - loss: 8.8409 - acc: 0.4373 - ETA: 0s - loss: 8.8316 - acc: 0.4377 - ETA: 0s - loss: 8.8282 - acc: 0.4378 - ETA: 0s - loss: 8.8218 - acc: 0.4382 - ETA: 0s - loss: 8.7978 - acc: 0.4397 - ETA: 0s - loss: 8.7867 - acc: 0.4403 - ETA: 0s - loss: 8.7869 - acc: 0.4398 - ETA: 0s - loss: 8.7843 - acc: 0.4398 - ETA: 0s - loss: 8.8029 - acc: 0.4386Epoch 00007: val_loss improved from 9.29745 to 9.23359, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.8031 - acc: 0.4386 - val_loss: 9.2336 - val_acc: 0.3617
Epoch 9/20
6580/6680 [============================>.] - ETA: 10s - loss: 8.8670 - acc: 0.4500 - ETA: 4s - loss: 8.9517 - acc: 0.4357  - ETA: 4s - loss: 9.4096 - acc: 0.4100 - ETA: 4s - loss: 8.9772 - acc: 0.4357 - ETA: 3s - loss: 9.0247 - acc: 0.4295 - ETA: 3s - loss: 8.7415 - acc: 0.4444 - ETA: 4s - loss: 8.7285 - acc: 0.4431 - ETA: 4s - loss: 8.6989 - acc: 0.4470 - ETA: 3s - loss: 8.7438 - acc: 0.4421 - ETA: 4s - loss: 8.7426 - acc: 0.4415 - ETA: 4s - loss: 8.8539 - acc: 0.4330 - ETA: 4s - loss: 8.9329 - acc: 0.4292 - ETA: 4s - loss: 8.8517 - acc: 0.4343 - ETA: 4s - loss: 8.8802 - acc: 0.4339 - ETA: 3s - loss: 8.9637 - acc: 0.4295 - ETA: 3s - loss: 8.8875 - acc: 0.4346 - ETA: 3s - loss: 8.8186 - acc: 0.4393 - ETA: 3s - loss: 8.8261 - acc: 0.4380 - ETA: 3s - loss: 8.7974 - acc: 0.4399 - ETA: 3s - loss: 8.8137 - acc: 0.4394 - ETA: 3s - loss: 8.8344 - acc: 0.4388 - ETA: 3s - loss: 8.8237 - acc: 0.4389 - ETA: 3s - loss: 8.7935 - acc: 0.4409 - ETA: 3s - loss: 8.7914 - acc: 0.4415 - ETA: 2s - loss: 8.8253 - acc: 0.4387 - ETA: 2s - loss: 8.8400 - acc: 0.4377 - ETA: 2s - loss: 8.8745 - acc: 0.4357 - ETA: 2s - loss: 8.8590 - acc: 0.4358 - ETA: 2s - loss: 8.8619 - acc: 0.4354 - ETA: 2s - loss: 8.8050 - acc: 0.4387 - ETA: 2s - loss: 8.7990 - acc: 0.4386 - ETA: 2s - loss: 8.8180 - acc: 0.4373 - ETA: 2s - loss: 8.8208 - acc: 0.4374 - ETA: 2s - loss: 8.7815 - acc: 0.4399 - ETA: 2s - loss: 8.7817 - acc: 0.4396 - ETA: 2s - loss: 8.7701 - acc: 0.4398 - ETA: 2s - loss: 8.7861 - acc: 0.4389 - ETA: 2s - loss: 8.8321 - acc: 0.4361 - ETA: 2s - loss: 8.8101 - acc: 0.4364 - ETA: 2s - loss: 8.7803 - acc: 0.4383 - ETA: 1s - loss: 8.8002 - acc: 0.4374 - ETA: 1s - loss: 8.8112 - acc: 0.4371 - ETA: 1s - loss: 8.8078 - acc: 0.4374 - ETA: 1s - loss: 8.7890 - acc: 0.4382 - ETA: 1s - loss: 8.7827 - acc: 0.4385 - ETA: 1s - loss: 8.7810 - acc: 0.4387 - ETA: 1s - loss: 8.7675 - acc: 0.4393 - ETA: 1s - loss: 8.7825 - acc: 0.4380 - ETA: 1s - loss: 8.7917 - acc: 0.4376 - ETA: 1s - loss: 8.8001 - acc: 0.4373 - ETA: 1s - loss: 8.7831 - acc: 0.4385 - ETA: 1s - loss: 8.7673 - acc: 0.4392 - ETA: 1s - loss: 8.7457 - acc: 0.4407 - ETA: 1s - loss: 8.7384 - acc: 0.4415 - ETA: 1s - loss: 8.7401 - acc: 0.4416 - ETA: 0s - loss: 8.7517 - acc: 0.4412 - ETA: 0s - loss: 8.7457 - acc: 0.4414 - ETA: 0s - loss: 8.7367 - acc: 0.4416 - ETA: 0s - loss: 8.7251 - acc: 0.4427 - ETA: 0s - loss: 8.7405 - acc: 0.4415 - ETA: 0s - loss: 8.7466 - acc: 0.4407 - ETA: 0s - loss: 8.7543 - acc: 0.4401 - ETA: 0s - loss: 8.7379 - acc: 0.4411 - ETA: 0s - loss: 8.7468 - acc: 0.4404 - ETA: 0s - loss: 8.7429 - acc: 0.4405 - ETA: 0s - loss: 8.7299 - acc: 0.4408 - ETA: 0s - loss: 8.7263 - acc: 0.4407 - ETA: 0s - loss: 8.7451 - acc: 0.4393 - ETA: 0s - loss: 8.7360 - acc: 0.4395 - ETA: 0s - loss: 8.7405 - acc: 0.4388 - ETA: 0s - loss: 8.7444 - acc: 0.4386 - ETA: 0s - loss: 8.7113 - acc: 0.4401Epoch 00008: val_loss improved from 9.23359 to 9.02218, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.6886 - acc: 0.4416 - val_loss: 9.0222 - val_acc: 0.3796
Epoch 10/20
6660/6680 [============================>.] - ETA: 0s - loss: 10.5346 - acc: 0.3000 - ETA: 3s - loss: 9.3029 - acc: 0.4000  - ETA: 3s - loss: 8.3217 - acc: 0.4625 - ETA: 3s - loss: 8.5911 - acc: 0.4471 - ETA: 3s - loss: 8.8870 - acc: 0.4286 - ETA: 3s - loss: 8.9226 - acc: 0.4271 - ETA: 3s - loss: 8.8080 - acc: 0.4367 - ETA: 3s - loss: 8.8697 - acc: 0.4303 - ETA: 3s - loss: 8.9012 - acc: 0.4270 - ETA: 3s - loss: 8.7942 - acc: 0.4321 - ETA: 3s - loss: 8.6261 - acc: 0.4449 - ETA: 3s - loss: 8.7019 - acc: 0.4396 - ETA: 3s - loss: 8.6731 - acc: 0.4412 - ETA: 3s - loss: 8.6976 - acc: 0.4403 - ETA: 3s - loss: 8.6463 - acc: 0.4432 - ETA: 3s - loss: 8.5509 - acc: 0.4500 - ETA: 3s - loss: 8.5747 - acc: 0.4493 - ETA: 3s - loss: 8.5930 - acc: 0.4481 - ETA: 3s - loss: 8.5243 - acc: 0.4529 - ETA: 3s - loss: 8.4873 - acc: 0.4556 - ETA: 3s - loss: 8.5418 - acc: 0.4532 - ETA: 3s - loss: 8.5342 - acc: 0.4536 - ETA: 3s - loss: 8.5026 - acc: 0.4563 - ETA: 3s - loss: 8.4776 - acc: 0.4580 - ETA: 2s - loss: 8.4282 - acc: 0.4608 - ETA: 2s - loss: 8.4060 - acc: 0.4618 - ETA: 2s - loss: 8.3198 - acc: 0.4668 - ETA: 2s - loss: 8.3218 - acc: 0.4665 - ETA: 2s - loss: 8.3128 - acc: 0.4671 - ETA: 2s - loss: 8.3180 - acc: 0.4673 - ETA: 2s - loss: 8.3304 - acc: 0.4654 - ETA: 2s - loss: 8.3578 - acc: 0.4638 - ETA: 2s - loss: 8.3832 - acc: 0.4624 - ETA: 2s - loss: 8.3765 - acc: 0.4618 - ETA: 2s - loss: 8.3905 - acc: 0.4608 - ETA: 2s - loss: 8.3665 - acc: 0.4626 - ETA: 2s - loss: 8.3873 - acc: 0.4609 - ETA: 2s - loss: 8.3522 - acc: 0.4626 - ETA: 2s - loss: 8.3843 - acc: 0.4609 - ETA: 2s - loss: 8.3838 - acc: 0.4607 - ETA: 1s - loss: 8.3820 - acc: 0.4606 - ETA: 1s - loss: 8.3722 - acc: 0.4619 - ETA: 1s - loss: 8.3854 - acc: 0.4613 - ETA: 1s - loss: 8.4290 - acc: 0.4585 - ETA: 1s - loss: 8.4643 - acc: 0.4567 - ETA: 1s - loss: 8.4785 - acc: 0.4558 - ETA: 1s - loss: 8.4849 - acc: 0.4552 - ETA: 1s - loss: 8.4669 - acc: 0.4560 - ETA: 1s - loss: 8.4419 - acc: 0.4576 - ETA: 1s - loss: 8.4336 - acc: 0.4581 - ETA: 1s - loss: 8.4719 - acc: 0.4557 - ETA: 1s - loss: 8.4759 - acc: 0.4556 - ETA: 1s - loss: 8.4983 - acc: 0.4539 - ETA: 1s - loss: 8.4760 - acc: 0.4551 - ETA: 1s - loss: 8.4681 - acc: 0.4553 - ETA: 0s - loss: 8.4559 - acc: 0.4559 - ETA: 0s - loss: 8.4551 - acc: 0.4559 - ETA: 0s - loss: 8.4476 - acc: 0.4566 - ETA: 0s - loss: 8.4372 - acc: 0.4572 - ETA: 0s - loss: 8.4419 - acc: 0.4571 - ETA: 0s - loss: 8.4331 - acc: 0.4579 - ETA: 0s - loss: 8.4208 - acc: 0.4584 - ETA: 0s - loss: 8.4281 - acc: 0.4581 - ETA: 0s - loss: 8.4293 - acc: 0.4583 - ETA: 0s - loss: 8.4245 - acc: 0.4589 - ETA: 0s - loss: 8.4429 - acc: 0.4575 - ETA: 0s - loss: 8.4515 - acc: 0.4567 - ETA: 0s - loss: 8.4646 - acc: 0.4560 - ETA: 0s - loss: 8.4842 - acc: 0.4547 - ETA: 0s - loss: 8.5050 - acc: 0.4534 - ETA: 0s - loss: 8.5223 - acc: 0.4524Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 8.5231 - acc: 0.4522 - val_loss: 9.0406 - val_acc: 0.3653
Epoch 11/20
6580/6680 [============================>.] - ETA: 5s - loss: 11.2840 - acc: 0.3000 - ETA: 3s - loss: 8.7094 - acc: 0.4500  - ETA: 3s - loss: 8.9745 - acc: 0.4346 - ETA: 3s - loss: 8.6074 - acc: 0.4563 - ETA: 3s - loss: 8.4145 - acc: 0.4667 - ETA: 3s - loss: 8.4281 - acc: 0.4640 - ETA: 4s - loss: 8.3446 - acc: 0.4685 - ETA: 4s - loss: 8.3988 - acc: 0.4650 - ETA: 4s - loss: 8.4099 - acc: 0.4667 - ETA: 3s - loss: 8.5007 - acc: 0.4607 - ETA: 3s - loss: 8.5399 - acc: 0.4592 - ETA: 3s - loss: 8.5917 - acc: 0.4558 - ETA: 3s - loss: 8.5374 - acc: 0.4560 - ETA: 3s - loss: 8.4582 - acc: 0.4603 - ETA: 3s - loss: 8.4108 - acc: 0.4642 - ETA: 3s - loss: 8.3630 - acc: 0.4674 - ETA: 3s - loss: 8.3778 - acc: 0.4669 - ETA: 3s - loss: 8.3530 - acc: 0.4691 - ETA: 3s - loss: 8.3059 - acc: 0.4730 - ETA: 3s - loss: 8.3609 - acc: 0.4692 - ETA: 3s - loss: 8.3862 - acc: 0.4667 - ETA: 3s - loss: 8.4196 - acc: 0.4644 - ETA: 2s - loss: 8.4872 - acc: 0.4584 - ETA: 2s - loss: 8.4864 - acc: 0.4590 - ETA: 2s - loss: 8.5094 - acc: 0.4581 - ETA: 2s - loss: 8.5255 - acc: 0.4571 - ETA: 2s - loss: 8.4735 - acc: 0.4605 - ETA: 2s - loss: 8.5169 - acc: 0.4581 - ETA: 2s - loss: 8.5132 - acc: 0.4585 - ETA: 2s - loss: 8.5304 - acc: 0.4576 - ETA: 2s - loss: 8.4941 - acc: 0.4597 - ETA: 2s - loss: 8.4921 - acc: 0.4594 - ETA: 2s - loss: 8.4631 - acc: 0.4613 - ETA: 2s - loss: 8.4452 - acc: 0.4620 - ETA: 2s - loss: 8.3930 - acc: 0.4653 - ETA: 2s - loss: 8.3740 - acc: 0.4669 - ETA: 1s - loss: 8.4129 - acc: 0.4645 - ETA: 1s - loss: 8.4297 - acc: 0.4637 - ETA: 1s - loss: 8.4372 - acc: 0.4636 - ETA: 1s - loss: 8.4126 - acc: 0.4652 - ETA: 1s - loss: 8.3998 - acc: 0.4661 - ETA: 1s - loss: 8.3904 - acc: 0.4668 - ETA: 1s - loss: 8.3806 - acc: 0.4671 - ETA: 1s - loss: 8.3980 - acc: 0.4663 - ETA: 1s - loss: 8.3857 - acc: 0.4670 - ETA: 1s - loss: 8.3612 - acc: 0.4685 - ETA: 1s - loss: 8.3535 - acc: 0.4688 - ETA: 1s - loss: 8.3733 - acc: 0.4675 - ETA: 1s - loss: 8.3901 - acc: 0.4664 - ETA: 1s - loss: 8.3949 - acc: 0.4661 - ETA: 1s - loss: 8.3921 - acc: 0.4662 - ETA: 1s - loss: 8.4025 - acc: 0.4659 - ETA: 1s - loss: 8.4320 - acc: 0.4639 - ETA: 1s - loss: 8.3982 - acc: 0.4659 - ETA: 1s - loss: 8.3980 - acc: 0.4656 - ETA: 0s - loss: 8.3411 - acc: 0.4692 - ETA: 0s - loss: 8.3390 - acc: 0.4691 - ETA: 0s - loss: 8.3366 - acc: 0.4693 - ETA: 0s - loss: 8.3436 - acc: 0.4688 - ETA: 0s - loss: 8.3533 - acc: 0.4683 - ETA: 0s - loss: 8.3677 - acc: 0.4676 - ETA: 0s - loss: 8.3706 - acc: 0.4672 - ETA: 0s - loss: 8.3681 - acc: 0.4676 - ETA: 0s - loss: 8.3684 - acc: 0.4677 - ETA: 0s - loss: 8.3868 - acc: 0.4665 - ETA: 0s - loss: 8.4143 - acc: 0.4650 - ETA: 0s - loss: 8.4394 - acc: 0.4636 - ETA: 0s - loss: 8.4438 - acc: 0.4634 - ETA: 0s - loss: 8.4429 - acc: 0.4637 - ETA: 0s - loss: 8.4427 - acc: 0.4637Epoch 00010: val_loss improved from 9.02218 to 8.97665, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.4356 - acc: 0.4641 - val_loss: 8.9767 - val_acc: 0.3760
Epoch 12/20
6560/6680 [============================>.] - ETA: 10s - loss: 7.2543 - acc: 0.5500 - ETA: 4s - loss: 8.4687 - acc: 0.4750  - ETA: 4s - loss: 8.2410 - acc: 0.4818 - ETA: 4s - loss: 8.1884 - acc: 0.4875 - ETA: 4s - loss: 8.2440 - acc: 0.4850 - ETA: 3s - loss: 8.1803 - acc: 0.4870 - ETA: 4s - loss: 8.2461 - acc: 0.4828 - ETA: 3s - loss: 8.4762 - acc: 0.4671 - ETA: 3s - loss: 8.4457 - acc: 0.4679 - ETA: 3s - loss: 8.3530 - acc: 0.4750 - ETA: 3s - loss: 8.3098 - acc: 0.4760 - ETA: 3s - loss: 8.2092 - acc: 0.4824 - ETA: 3s - loss: 8.2127 - acc: 0.4787 - ETA: 3s - loss: 8.1550 - acc: 0.4823 - ETA: 3s - loss: 8.0311 - acc: 0.4893 - ETA: 3s - loss: 7.9992 - acc: 0.4900 - ETA: 3s - loss: 8.0297 - acc: 0.4875 - ETA: 3s - loss: 8.0791 - acc: 0.4851 - ETA: 3s - loss: 8.1249 - acc: 0.4828 - ETA: 3s - loss: 8.1237 - acc: 0.4828 - ETA: 3s - loss: 8.2148 - acc: 0.4771 - ETA: 3s - loss: 8.2131 - acc: 0.4766 - ETA: 3s - loss: 8.2980 - acc: 0.4718 - ETA: 3s - loss: 8.2769 - acc: 0.4734 - ETA: 2s - loss: 8.3144 - acc: 0.4709 - ETA: 2s - loss: 8.3700 - acc: 0.4672 - ETA: 2s - loss: 8.3307 - acc: 0.4702 - ETA: 2s - loss: 8.3131 - acc: 0.4711 - ETA: 2s - loss: 8.3542 - acc: 0.4686 - ETA: 2s - loss: 8.3936 - acc: 0.4660 - ETA: 2s - loss: 8.3774 - acc: 0.4667 - ETA: 2s - loss: 8.3739 - acc: 0.4670 - ETA: 2s - loss: 8.4112 - acc: 0.4648 - ETA: 2s - loss: 8.4002 - acc: 0.4655 - ETA: 2s - loss: 8.4114 - acc: 0.4652 - ETA: 2s - loss: 8.4474 - acc: 0.4628 - ETA: 2s - loss: 8.4387 - acc: 0.4635 - ETA: 2s - loss: 8.4230 - acc: 0.4640 - ETA: 2s - loss: 8.3995 - acc: 0.4658 - ETA: 1s - loss: 8.3788 - acc: 0.4668 - ETA: 1s - loss: 8.3874 - acc: 0.4666 - ETA: 1s - loss: 8.3835 - acc: 0.4668 - ETA: 1s - loss: 8.3530 - acc: 0.4690 - ETA: 1s - loss: 8.3061 - acc: 0.4721 - ETA: 1s - loss: 8.3026 - acc: 0.4725 - ETA: 1s - loss: 8.2677 - acc: 0.4741 - ETA: 1s - loss: 8.2772 - acc: 0.4734 - ETA: 1s - loss: 8.2580 - acc: 0.4742 - ETA: 1s - loss: 8.2726 - acc: 0.4735 - ETA: 1s - loss: 8.2795 - acc: 0.4732 - ETA: 1s - loss: 8.3402 - acc: 0.4691 - ETA: 1s - loss: 8.3434 - acc: 0.4690 - ETA: 1s - loss: 8.3500 - acc: 0.4684 - ETA: 1s - loss: 8.3396 - acc: 0.4690 - ETA: 1s - loss: 8.3317 - acc: 0.4696 - ETA: 0s - loss: 8.3330 - acc: 0.4697 - ETA: 0s - loss: 8.3504 - acc: 0.4689 - ETA: 0s - loss: 8.3487 - acc: 0.4687 - ETA: 0s - loss: 8.3443 - acc: 0.4691 - ETA: 0s - loss: 8.3463 - acc: 0.4690 - ETA: 0s - loss: 8.3346 - acc: 0.4699 - ETA: 0s - loss: 8.3001 - acc: 0.4718 - ETA: 0s - loss: 8.3124 - acc: 0.4710 - ETA: 0s - loss: 8.2979 - acc: 0.4720 - ETA: 0s - loss: 8.2976 - acc: 0.4722 - ETA: 0s - loss: 8.2952 - acc: 0.4724 - ETA: 0s - loss: 8.2800 - acc: 0.4731 - ETA: 0s - loss: 8.2996 - acc: 0.4721 - ETA: 0s - loss: 8.3106 - acc: 0.4715 - ETA: 0s - loss: 8.3074 - acc: 0.4718 - ETA: 0s - loss: 8.3218 - acc: 0.4711 - ETA: 0s - loss: 8.3218 - acc: 0.4712Epoch 00011: val_loss improved from 8.97665 to 8.86854, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.3509 - acc: 0.4696 - val_loss: 8.8685 - val_acc: 0.3952
Epoch 13/20
6660/6680 [============================>.] - ETA: 6s - loss: 5.6573 - acc: 0.6500 - ETA: 3s - loss: 7.9518 - acc: 0.5071 - ETA: 4s - loss: 8.7102 - acc: 0.4600 - ETA: 3s - loss: 8.4687 - acc: 0.4750 - ETA: 4s - loss: 7.9982 - acc: 0.5022 - ETA: 4s - loss: 8.0685 - acc: 0.4979 - ETA: 4s - loss: 8.3455 - acc: 0.4810 - ETA: 4s - loss: 8.4581 - acc: 0.4742 - ETA: 4s - loss: 8.3215 - acc: 0.4829 - ETA: 4s - loss: 8.4222 - acc: 0.4767 - ETA: 4s - loss: 8.3465 - acc: 0.4815 - ETA: 3s - loss: 8.3027 - acc: 0.4843 - ETA: 3s - loss: 8.3409 - acc: 0.4804 - ETA: 3s - loss: 8.4390 - acc: 0.4729 - ETA: 3s - loss: 8.4092 - acc: 0.4738 - ETA: 3s - loss: 8.4604 - acc: 0.4709 - ETA: 3s - loss: 8.4668 - acc: 0.4708 - ETA: 3s - loss: 8.4269 - acc: 0.4724 - ETA: 3s - loss: 8.4496 - acc: 0.4713 - ETA: 3s - loss: 8.4412 - acc: 0.4713 - ETA: 3s - loss: 8.4272 - acc: 0.4718 - ETA: 3s - loss: 8.4623 - acc: 0.4695 - ETA: 2s - loss: 8.4778 - acc: 0.4689 - ETA: 2s - loss: 8.4347 - acc: 0.4714 - ETA: 2s - loss: 8.4560 - acc: 0.4703 - ETA: 2s - loss: 8.4131 - acc: 0.4728 - ETA: 2s - loss: 8.4020 - acc: 0.4736 - ETA: 2s - loss: 8.4018 - acc: 0.4729 - ETA: 2s - loss: 8.4361 - acc: 0.4710 - ETA: 2s - loss: 8.4107 - acc: 0.4727 - ETA: 2s - loss: 8.3668 - acc: 0.4753 - ETA: 2s - loss: 8.3726 - acc: 0.4745 - ETA: 2s - loss: 8.3324 - acc: 0.4765 - ETA: 2s - loss: 8.3190 - acc: 0.4771 - ETA: 2s - loss: 8.2965 - acc: 0.4784 - ETA: 2s - loss: 8.2876 - acc: 0.4788 - ETA: 2s - loss: 8.2667 - acc: 0.4803 - ETA: 2s - loss: 8.2793 - acc: 0.4795 - ETA: 2s - loss: 8.2791 - acc: 0.4797 - ETA: 1s - loss: 8.2628 - acc: 0.4805 - ETA: 1s - loss: 8.2945 - acc: 0.4788 - ETA: 1s - loss: 8.2982 - acc: 0.4781 - ETA: 1s - loss: 8.3081 - acc: 0.4772 - ETA: 1s - loss: 8.3037 - acc: 0.4777 - ETA: 1s - loss: 8.3030 - acc: 0.4779 - ETA: 1s - loss: 8.3043 - acc: 0.4772 - ETA: 1s - loss: 8.2840 - acc: 0.4787 - ETA: 1s - loss: 8.2756 - acc: 0.4792 - ETA: 1s - loss: 8.2871 - acc: 0.4786 - ETA: 1s - loss: 8.3356 - acc: 0.4758 - ETA: 1s - loss: 8.3260 - acc: 0.4761 - ETA: 1s - loss: 8.3306 - acc: 0.4756 - ETA: 1s - loss: 8.3239 - acc: 0.4761 - ETA: 1s - loss: 8.3438 - acc: 0.4750 - ETA: 1s - loss: 8.3632 - acc: 0.4739 - ETA: 1s - loss: 8.3605 - acc: 0.4740 - ETA: 1s - loss: 8.3609 - acc: 0.4741 - ETA: 1s - loss: 8.3577 - acc: 0.4742 - ETA: 0s - loss: 8.3774 - acc: 0.4730 - ETA: 0s - loss: 8.3745 - acc: 0.4728 - ETA: 0s - loss: 8.3835 - acc: 0.4724 - ETA: 0s - loss: 8.3742 - acc: 0.4731 - ETA: 0s - loss: 8.3542 - acc: 0.4745 - ETA: 0s - loss: 8.3613 - acc: 0.4740 - ETA: 0s - loss: 8.3385 - acc: 0.4755 - ETA: 0s - loss: 8.3347 - acc: 0.4754 - ETA: 0s - loss: 8.3375 - acc: 0.4750 - ETA: 0s - loss: 8.3505 - acc: 0.4740 - ETA: 0s - loss: 8.3363 - acc: 0.4750 - ETA: 0s - loss: 8.3526 - acc: 0.4739 - ETA: 0s - loss: 8.3579 - acc: 0.4737 - ETA: 0s - loss: 8.3416 - acc: 0.4748 - ETA: 0s - loss: 8.3453 - acc: 0.4746 - ETA: 0s - loss: 8.3336 - acc: 0.4755 - ETA: 0s - loss: 8.3131 - acc: 0.4767Epoch 00012: val_loss improved from 8.86854 to 8.81100, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.3147 - acc: 0.4766 - val_loss: 8.8110 - val_acc: 0.3976
Epoch 14/20
6600/6680 [============================>.] - ETA: 0s - loss: 4.0385 - acc: 0.7500 - ETA: 5s - loss: 6.3127 - acc: 0.6000 - ETA: 4s - loss: 7.3342 - acc: 0.5389 - ETA: 4s - loss: 7.4979 - acc: 0.5308 - ETA: 4s - loss: 7.8032 - acc: 0.5132 - ETA: 4s - loss: 7.9557 - acc: 0.5022 - ETA: 4s - loss: 8.1193 - acc: 0.4929 - ETA: 3s - loss: 8.4408 - acc: 0.4735 - ETA: 3s - loss: 8.3846 - acc: 0.4756 - ETA: 3s - loss: 8.4080 - acc: 0.4733 - ETA: 3s - loss: 8.4320 - acc: 0.4724 - ETA: 3s - loss: 8.4234 - acc: 0.4726 - ETA: 3s - loss: 8.3696 - acc: 0.4763 - ETA: 3s - loss: 8.3045 - acc: 0.4805 - ETA: 3s - loss: 8.3658 - acc: 0.4770 - ETA: 3s - loss: 8.3327 - acc: 0.4787 - ETA: 3s - loss: 8.3416 - acc: 0.4781 - ETA: 3s - loss: 8.3866 - acc: 0.4756 - ETA: 3s - loss: 8.3788 - acc: 0.4762 - ETA: 3s - loss: 8.3885 - acc: 0.4759 - ETA: 3s - loss: 8.4671 - acc: 0.4711 - ETA: 3s - loss: 8.4280 - acc: 0.4737 - ETA: 3s - loss: 8.4711 - acc: 0.4711 - ETA: 3s - loss: 8.4708 - acc: 0.4713 - ETA: 3s - loss: 8.4556 - acc: 0.4719 - ETA: 3s - loss: 8.4151 - acc: 0.4745 - ETA: 3s - loss: 8.3795 - acc: 0.4768 - ETA: 3s - loss: 8.3543 - acc: 0.4785 - ETA: 3s - loss: 8.3707 - acc: 0.4773 - ETA: 2s - loss: 8.3520 - acc: 0.4782 - ETA: 2s - loss: 8.3959 - acc: 0.4754 - ETA: 2s - loss: 8.3609 - acc: 0.4777 - ETA: 2s - loss: 8.3727 - acc: 0.4766 - ETA: 2s - loss: 8.3373 - acc: 0.4787 - ETA: 2s - loss: 8.2966 - acc: 0.4810 - ETA: 2s - loss: 8.2886 - acc: 0.4812 - ETA: 2s - loss: 8.3312 - acc: 0.4776 - ETA: 2s - loss: 8.3616 - acc: 0.4756 - ETA: 2s - loss: 8.3402 - acc: 0.4765 - ETA: 2s - loss: 8.3458 - acc: 0.4759 - ETA: 2s - loss: 8.3714 - acc: 0.4743 - ETA: 2s - loss: 8.3675 - acc: 0.4741 - ETA: 2s - loss: 8.3664 - acc: 0.4742 - ETA: 2s - loss: 8.3703 - acc: 0.4741 - ETA: 2s - loss: 8.3618 - acc: 0.4743 - ETA: 1s - loss: 8.3418 - acc: 0.4758 - ETA: 1s - loss: 8.3516 - acc: 0.4752 - ETA: 1s - loss: 8.3105 - acc: 0.4775 - ETA: 1s - loss: 8.3306 - acc: 0.4765 - ETA: 1s - loss: 8.3330 - acc: 0.4765 - ETA: 1s - loss: 8.3223 - acc: 0.4773 - ETA: 1s - loss: 8.3048 - acc: 0.4782 - ETA: 1s - loss: 8.3053 - acc: 0.4778 - ETA: 1s - loss: 8.3101 - acc: 0.4775 - ETA: 1s - loss: 8.3052 - acc: 0.4777 - ETA: 1s - loss: 8.3159 - acc: 0.4769 - ETA: 1s - loss: 8.3267 - acc: 0.4760 - ETA: 1s - loss: 8.3367 - acc: 0.4753 - ETA: 1s - loss: 8.3305 - acc: 0.4754 - ETA: 0s - loss: 8.3100 - acc: 0.4763 - ETA: 0s - loss: 8.3234 - acc: 0.4755 - ETA: 0s - loss: 8.3206 - acc: 0.4757 - ETA: 0s - loss: 8.3152 - acc: 0.4757 - ETA: 0s - loss: 8.3255 - acc: 0.4751 - ETA: 0s - loss: 8.3349 - acc: 0.4747 - ETA: 0s - loss: 8.3345 - acc: 0.4746 - ETA: 0s - loss: 8.3165 - acc: 0.4754 - ETA: 0s - loss: 8.3108 - acc: 0.4758 - ETA: 0s - loss: 8.3105 - acc: 0.4757 - ETA: 0s - loss: 8.3003 - acc: 0.4763 - ETA: 0s - loss: 8.2836 - acc: 0.4773 - ETA: 0s - loss: 8.2711 - acc: 0.4782 - ETA: 0s - loss: 8.2749 - acc: 0.4780 - ETA: 0s - loss: 8.2910 - acc: 0.4772 - ETA: 0s - loss: 8.2875 - acc: 0.4775 - ETA: 0s - loss: 8.2740 - acc: 0.4780Epoch 00013: val_loss improved from 8.81100 to 8.77454, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.2787 - acc: 0.4777 - val_loss: 8.7745 - val_acc: 0.3892
Epoch 15/20
6600/6680 [============================>.] - ETA: 6s - loss: 8.0606 - acc: 0.5000 - ETA: 3s - loss: 8.7751 - acc: 0.4500 - ETA: 4s - loss: 8.4363 - acc: 0.4727 - ETA: 4s - loss: 8.5511 - acc: 0.4667 - ETA: 4s - loss: 8.6482 - acc: 0.4611 - ETA: 4s - loss: 8.6956 - acc: 0.4587 - ETA: 4s - loss: 8.6594 - acc: 0.4596 - ETA: 4s - loss: 8.4036 - acc: 0.4759 - ETA: 4s - loss: 8.2625 - acc: 0.4833 - ETA: 4s - loss: 8.4329 - acc: 0.4731 - ETA: 4s - loss: 8.4446 - acc: 0.4726 - ETA: 4s - loss: 8.4049 - acc: 0.4739 - ETA: 4s - loss: 8.3208 - acc: 0.4780 - ETA: 3s - loss: 8.2559 - acc: 0.4821 - ETA: 3s - loss: 8.2780 - acc: 0.4798 - ETA: 3s - loss: 8.2027 - acc: 0.4843 - ETA: 3s - loss: 8.1843 - acc: 0.4840 - ETA: 3s - loss: 8.1794 - acc: 0.4847 - ETA: 3s - loss: 8.1434 - acc: 0.4867 - ETA: 3s - loss: 8.1307 - acc: 0.4878 - ETA: 3s - loss: 8.1898 - acc: 0.4843 - ETA: 3s - loss: 8.1641 - acc: 0.4858 - ETA: 3s - loss: 8.1527 - acc: 0.4867 - ETA: 3s - loss: 8.1187 - acc: 0.4886 - ETA: 3s - loss: 8.0553 - acc: 0.4929 - ETA: 3s - loss: 8.0701 - acc: 0.4923 - ETA: 3s - loss: 8.0918 - acc: 0.4909 - ETA: 3s - loss: 8.0638 - acc: 0.4929 - ETA: 2s - loss: 8.0771 - acc: 0.4923 - ETA: 2s - loss: 8.0668 - acc: 0.4930 - ETA: 2s - loss: 8.0748 - acc: 0.4920 - ETA: 2s - loss: 8.0691 - acc: 0.4926 - ETA: 2s - loss: 8.0712 - acc: 0.4927 - ETA: 2s - loss: 8.0673 - acc: 0.4927 - ETA: 2s - loss: 8.0670 - acc: 0.4929 - ETA: 2s - loss: 8.0659 - acc: 0.4925 - ETA: 2s - loss: 8.0369 - acc: 0.4945 - ETA: 2s - loss: 8.0586 - acc: 0.4932 - ETA: 2s - loss: 8.0772 - acc: 0.4923 - ETA: 2s - loss: 8.1037 - acc: 0.4908 - ETA: 2s - loss: 8.1298 - acc: 0.4893 - ETA: 2s - loss: 8.0981 - acc: 0.4914 - ETA: 1s - loss: 8.0602 - acc: 0.4927 - ETA: 1s - loss: 8.0728 - acc: 0.4920 - ETA: 1s - loss: 8.0610 - acc: 0.4926 - ETA: 1s - loss: 8.0977 - acc: 0.4901 - ETA: 1s - loss: 8.1362 - acc: 0.4879 - ETA: 1s - loss: 8.1469 - acc: 0.4872 - ETA: 1s - loss: 8.1496 - acc: 0.4865 - ETA: 1s - loss: 8.1589 - acc: 0.4859 - ETA: 1s - loss: 8.1257 - acc: 0.4877 - ETA: 1s - loss: 8.1213 - acc: 0.4879 - ETA: 1s - loss: 8.1202 - acc: 0.4879 - ETA: 1s - loss: 8.1305 - acc: 0.4872 - ETA: 1s - loss: 8.1465 - acc: 0.4862 - ETA: 1s - loss: 8.1578 - acc: 0.4855 - ETA: 1s - loss: 8.1675 - acc: 0.4847 - ETA: 1s - loss: 8.1793 - acc: 0.4839 - ETA: 1s - loss: 8.1766 - acc: 0.4839 - ETA: 1s - loss: 8.1675 - acc: 0.4843 - ETA: 0s - loss: 8.1872 - acc: 0.4826 - ETA: 0s - loss: 8.2053 - acc: 0.4812 - ETA: 0s - loss: 8.1843 - acc: 0.4825 - ETA: 0s - loss: 8.1938 - acc: 0.4821 - ETA: 0s - loss: 8.2160 - acc: 0.4809 - ETA: 0s - loss: 8.2265 - acc: 0.4803 - ETA: 0s - loss: 8.2072 - acc: 0.4812 - ETA: 0s - loss: 8.2211 - acc: 0.4805 - ETA: 0s - loss: 8.2273 - acc: 0.4801 - ETA: 0s - loss: 8.2345 - acc: 0.4797 - ETA: 0s - loss: 8.2299 - acc: 0.4799 - ETA: 0s - loss: 8.2262 - acc: 0.4802 - ETA: 0s - loss: 8.2037 - acc: 0.4815 - ETA: 0s - loss: 8.1960 - acc: 0.4817 - ETA: 0s - loss: 8.1716 - acc: 0.4832Epoch 00014: val_loss improved from 8.77454 to 8.68804, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.1831 - acc: 0.4825 - val_loss: 8.6880 - val_acc: 0.3916
Epoch 16/20
6620/6680 [============================>.] - ETA: 5s - loss: 7.2560 - acc: 0.5500 - ETA: 4s - loss: 6.5441 - acc: 0.5750 - ETA: 3s - loss: 6.9629 - acc: 0.5571 - ETA: 3s - loss: 7.2095 - acc: 0.5447 - ETA: 3s - loss: 7.4539 - acc: 0.5313 - ETA: 3s - loss: 7.6153 - acc: 0.5207 - ETA: 3s - loss: 7.6941 - acc: 0.5167 - ETA: 3s - loss: 7.6686 - acc: 0.5189 - ETA: 3s - loss: 7.7861 - acc: 0.5122 - ETA: 3s - loss: 7.7637 - acc: 0.5138 - ETA: 3s - loss: 7.7474 - acc: 0.5143 - ETA: 3s - loss: 7.7603 - acc: 0.5137 - ETA: 4s - loss: 7.7530 - acc: 0.5145 - ETA: 3s - loss: 7.7682 - acc: 0.5136 - ETA: 4s - loss: 7.7702 - acc: 0.5137 - ETA: 3s - loss: 7.7392 - acc: 0.5159 - ETA: 3s - loss: 7.7418 - acc: 0.5157 - ETA: 3s - loss: 7.7893 - acc: 0.5120 - ETA: 3s - loss: 7.8068 - acc: 0.5110 - ETA: 3s - loss: 7.8122 - acc: 0.5100 - ETA: 3s - loss: 7.8108 - acc: 0.5105 - ETA: 3s - loss: 7.8688 - acc: 0.5067 - ETA: 3s - loss: 7.8976 - acc: 0.5048 - ETA: 3s - loss: 7.8724 - acc: 0.5057 - ETA: 3s - loss: 7.8857 - acc: 0.5045 - ETA: 3s - loss: 7.9711 - acc: 0.4986 - ETA: 3s - loss: 8.0022 - acc: 0.4969 - ETA: 3s - loss: 8.0460 - acc: 0.4944 - ETA: 3s - loss: 8.0321 - acc: 0.4954 - ETA: 3s - loss: 8.0463 - acc: 0.4947 - ETA: 3s - loss: 8.0371 - acc: 0.4953 - ETA: 2s - loss: 8.0873 - acc: 0.4922 - ETA: 2s - loss: 8.0470 - acc: 0.4946 - ETA: 2s - loss: 8.0373 - acc: 0.4951 - ETA: 2s - loss: 8.0627 - acc: 0.4929 - ETA: 2s - loss: 8.0389 - acc: 0.4942 - ETA: 2s - loss: 8.0504 - acc: 0.4933 - ETA: 2s - loss: 8.0330 - acc: 0.4938 - ETA: 2s - loss: 7.9979 - acc: 0.4961 - ETA: 2s - loss: 7.9993 - acc: 0.4962 - ETA: 2s - loss: 8.0007 - acc: 0.4963 - ETA: 2s - loss: 8.0281 - acc: 0.4947 - ETA: 2s - loss: 8.0465 - acc: 0.4937 - ETA: 2s - loss: 8.0687 - acc: 0.4923 - ETA: 2s - loss: 8.0771 - acc: 0.4918 - ETA: 2s - loss: 8.0727 - acc: 0.4922 - ETA: 2s - loss: 8.0889 - acc: 0.4913 - ETA: 1s - loss: 8.0562 - acc: 0.4935 - ETA: 1s - loss: 8.0681 - acc: 0.4929 - ETA: 1s - loss: 8.0958 - acc: 0.4909 - ETA: 1s - loss: 8.0910 - acc: 0.4914 - ETA: 1s - loss: 8.1097 - acc: 0.4902 - ETA: 1s - loss: 8.0981 - acc: 0.4911 - ETA: 1s - loss: 8.0915 - acc: 0.4913 - ETA: 1s - loss: 8.1147 - acc: 0.4899 - ETA: 1s - loss: 8.1038 - acc: 0.4907 - ETA: 1s - loss: 8.1260 - acc: 0.4894 - ETA: 1s - loss: 8.1343 - acc: 0.4890 - ETA: 1s - loss: 8.1368 - acc: 0.4888 - ETA: 1s - loss: 8.1681 - acc: 0.4868 - ETA: 0s - loss: 8.1510 - acc: 0.4878 - ETA: 0s - loss: 8.1250 - acc: 0.4893 - ETA: 0s - loss: 8.1232 - acc: 0.4893 - ETA: 0s - loss: 8.1224 - acc: 0.4892 - ETA: 0s - loss: 8.1363 - acc: 0.4882 - ETA: 0s - loss: 8.1153 - acc: 0.4897 - ETA: 0s - loss: 8.1037 - acc: 0.4905 - ETA: 0s - loss: 8.1170 - acc: 0.4895 - ETA: 0s - loss: 8.1032 - acc: 0.4903 - ETA: 0s - loss: 8.1087 - acc: 0.4899 - ETA: 0s - loss: 8.1192 - acc: 0.4892 - ETA: 0s - loss: 8.0965 - acc: 0.4903 - ETA: 0s - loss: 8.1035 - acc: 0.4897 - ETA: 0s - loss: 8.0918 - acc: 0.4903 - ETA: 0s - loss: 8.0836 - acc: 0.4906Epoch 00015: val_loss improved from 8.68804 to 8.65262, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.0692 - acc: 0.4915 - val_loss: 8.6526 - val_acc: 0.4108
Epoch 17/20
6600/6680 [============================>.] - ETA: 10s - loss: 6.4485 - acc: 0.6000 - ETA: 9s - loss: 6.4604 - acc: 0.6000  - ETA: 7s - loss: 7.7212 - acc: 0.5214 - ETA: 6s - loss: 8.0635 - acc: 0.5000 - ETA: 6s - loss: 8.0631 - acc: 0.5000 - ETA: 5s - loss: 8.0234 - acc: 0.5025 - ETA: 4s - loss: 8.2579 - acc: 0.4846 - ETA: 4s - loss: 8.1778 - acc: 0.4900 - ETA: 4s - loss: 8.2403 - acc: 0.4864 - ETA: 4s - loss: 8.0492 - acc: 0.4974 - ETA: 4s - loss: 8.1768 - acc: 0.4889 - ETA: 3s - loss: 8.1645 - acc: 0.4892 - ETA: 3s - loss: 8.1156 - acc: 0.4921 - ETA: 3s - loss: 8.0860 - acc: 0.4942 - ETA: 3s - loss: 7.9999 - acc: 0.5000 - ETA: 3s - loss: 7.9953 - acc: 0.4993 - ETA: 3s - loss: 8.0104 - acc: 0.4987 - ETA: 3s - loss: 7.9580 - acc: 0.5000 - ETA: 3s - loss: 7.9690 - acc: 0.4994 - ETA: 3s - loss: 8.0698 - acc: 0.4927 - ETA: 3s - loss: 8.0370 - acc: 0.4946 - ETA: 3s - loss: 8.0300 - acc: 0.4954 - ETA: 3s - loss: 7.9906 - acc: 0.4980 - ETA: 3s - loss: 8.0319 - acc: 0.4956 - ETA: 3s - loss: 8.0465 - acc: 0.4939 - ETA: 3s - loss: 8.0615 - acc: 0.4934 - ETA: 3s - loss: 8.0708 - acc: 0.4924 - ETA: 2s - loss: 8.1396 - acc: 0.4879 - ETA: 2s - loss: 8.1192 - acc: 0.4893 - ETA: 2s - loss: 8.1348 - acc: 0.4886 - ETA: 2s - loss: 8.0680 - acc: 0.4928 - ETA: 2s - loss: 8.1330 - acc: 0.4888 - ETA: 2s - loss: 8.2068 - acc: 0.4845 - ETA: 2s - loss: 8.2200 - acc: 0.4838 - ETA: 2s - loss: 8.2563 - acc: 0.4817 - ETA: 2s - loss: 8.2552 - acc: 0.4820 - ETA: 2s - loss: 8.2380 - acc: 0.4831 - ETA: 2s - loss: 8.2126 - acc: 0.4849 - ETA: 2s - loss: 8.2061 - acc: 0.4854 - ETA: 2s - loss: 8.2070 - acc: 0.4852 - ETA: 2s - loss: 8.1878 - acc: 0.4864 - ETA: 2s - loss: 8.1793 - acc: 0.4871 - ETA: 1s - loss: 8.1883 - acc: 0.4867 - ETA: 1s - loss: 8.1939 - acc: 0.4865 - ETA: 1s - loss: 8.1916 - acc: 0.4866 - ETA: 1s - loss: 8.1818 - acc: 0.4871 - ETA: 1s - loss: 8.1795 - acc: 0.4874 - ETA: 1s - loss: 8.1476 - acc: 0.4891 - ETA: 1s - loss: 8.1594 - acc: 0.4885 - ETA: 1s - loss: 8.1509 - acc: 0.4891 - ETA: 1s - loss: 8.1418 - acc: 0.4898 - ETA: 1s - loss: 8.1502 - acc: 0.4893 - ETA: 1s - loss: 8.1426 - acc: 0.4895 - ETA: 1s - loss: 8.1282 - acc: 0.4905 - ETA: 1s - loss: 8.1275 - acc: 0.4906 - ETA: 1s - loss: 8.1420 - acc: 0.4893 - ETA: 0s - loss: 8.1435 - acc: 0.4893 - ETA: 0s - loss: 8.1087 - acc: 0.4916 - ETA: 0s - loss: 8.1139 - acc: 0.4912 - ETA: 0s - loss: 8.1074 - acc: 0.4917 - ETA: 0s - loss: 8.0752 - acc: 0.4936 - ETA: 0s - loss: 8.0530 - acc: 0.4950 - ETA: 0s - loss: 8.0430 - acc: 0.4956 - ETA: 0s - loss: 8.0139 - acc: 0.4975 - ETA: 0s - loss: 7.9959 - acc: 0.4987 - ETA: 0s - loss: 8.0052 - acc: 0.4981 - ETA: 0s - loss: 7.9907 - acc: 0.4990 - ETA: 0s - loss: 8.0052 - acc: 0.4981 - ETA: 0s - loss: 8.0256 - acc: 0.4965Epoch 00016: val_loss improved from 8.65262 to 8.64941, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.0339 - acc: 0.4958 - val_loss: 8.6494 - val_acc: 0.4036
Epoch 18/20
6660/6680 [============================>.] - ETA: 10s - loss: 8.0593 - acc: 0.5000 - ETA: 6s - loss: 8.8651 - acc: 0.4500  - ETA: 4s - loss: 8.0641 - acc: 0.4950 - ETA: 4s - loss: 8.2498 - acc: 0.4846 - ETA: 4s - loss: 8.1164 - acc: 0.4917 - ETA: 4s - loss: 8.1971 - acc: 0.4880 - ETA: 3s - loss: 7.9889 - acc: 0.5016 - ETA: 3s - loss: 7.8941 - acc: 0.5066 - ETA: 3s - loss: 7.9901 - acc: 0.5011 - ETA: 3s - loss: 7.9431 - acc: 0.5043 - ETA: 3s - loss: 7.9543 - acc: 0.5038 - ETA: 3s - loss: 7.9747 - acc: 0.5027 - ETA: 3s - loss: 7.9433 - acc: 0.5034 - ETA: 3s - loss: 7.9273 - acc: 0.5047 - ETA: 3s - loss: 7.8859 - acc: 0.5075 - ETA: 3s - loss: 7.8358 - acc: 0.5107 - ETA: 3s - loss: 7.9173 - acc: 0.5059 - ETA: 3s - loss: 7.9479 - acc: 0.5037 - ETA: 3s - loss: 7.9482 - acc: 0.5035 - ETA: 3s - loss: 8.0076 - acc: 0.5000 - ETA: 3s - loss: 8.0026 - acc: 0.5000 - ETA: 3s - loss: 8.0131 - acc: 0.4995 - ETA: 2s - loss: 8.0479 - acc: 0.4971 - ETA: 2s - loss: 8.0705 - acc: 0.4959 - ETA: 2s - loss: 8.0631 - acc: 0.4965 - ETA: 2s - loss: 8.0835 - acc: 0.4953 - ETA: 2s - loss: 8.1224 - acc: 0.4930 - ETA: 2s - loss: 8.1332 - acc: 0.4925 - ETA: 2s - loss: 8.1130 - acc: 0.4935 - ETA: 2s - loss: 8.0696 - acc: 0.4964 - ETA: 2s - loss: 8.0808 - acc: 0.4955 - ETA: 2s - loss: 8.1025 - acc: 0.4939 - ETA: 2s - loss: 8.0802 - acc: 0.4954 - ETA: 2s - loss: 8.0963 - acc: 0.4942 - ETA: 2s - loss: 8.0802 - acc: 0.4953 - ETA: 2s - loss: 8.0653 - acc: 0.4961 - ETA: 2s - loss: 8.0204 - acc: 0.4983 - ETA: 1s - loss: 8.0304 - acc: 0.4977 - ETA: 1s - loss: 8.0268 - acc: 0.4981 - ETA: 1s - loss: 8.0145 - acc: 0.4989 - ETA: 1s - loss: 8.0111 - acc: 0.4992 - ETA: 1s - loss: 8.0199 - acc: 0.4987 - ETA: 1s - loss: 8.0250 - acc: 0.4985 - ETA: 1s - loss: 8.0215 - acc: 0.4988 - ETA: 1s - loss: 7.9988 - acc: 0.5002 - ETA: 1s - loss: 8.0088 - acc: 0.4993 - ETA: 1s - loss: 7.9916 - acc: 0.5002 - ETA: 1s - loss: 7.9611 - acc: 0.5023 - ETA: 1s - loss: 7.9740 - acc: 0.5013 - ETA: 1s - loss: 7.9928 - acc: 0.5002 - ETA: 1s - loss: 8.0185 - acc: 0.4987 - ETA: 1s - loss: 8.0050 - acc: 0.4992 - ETA: 1s - loss: 8.0159 - acc: 0.4986 - ETA: 1s - loss: 8.0157 - acc: 0.4980 - ETA: 1s - loss: 8.0161 - acc: 0.4980 - ETA: 1s - loss: 8.0337 - acc: 0.4969 - ETA: 1s - loss: 8.0344 - acc: 0.4967 - ETA: 0s - loss: 8.0353 - acc: 0.4966 - ETA: 0s - loss: 8.0560 - acc: 0.4954 - ETA: 0s - loss: 8.0623 - acc: 0.4949 - ETA: 0s - loss: 8.0419 - acc: 0.4962 - ETA: 0s - loss: 8.0204 - acc: 0.4973 - ETA: 0s - loss: 8.0267 - acc: 0.4969 - ETA: 0s - loss: 8.0187 - acc: 0.4974 - ETA: 0s - loss: 8.0191 - acc: 0.4974 - ETA: 0s - loss: 8.0326 - acc: 0.4965 - ETA: 0s - loss: 8.0390 - acc: 0.4960 - ETA: 0s - loss: 8.0472 - acc: 0.4956 - ETA: 0s - loss: 8.0502 - acc: 0.4953 - ETA: 0s - loss: 8.0375 - acc: 0.4962 - ETA: 0s - loss: 8.0212 - acc: 0.4972 - ETA: 0s - loss: 8.0072 - acc: 0.4978 - ETA: 0s - loss: 8.0284 - acc: 0.4965 - ETA: 0s - loss: 8.0203 - acc: 0.4967Epoch 00017: val_loss improved from 8.64941 to 8.61558, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 8.0180 - acc: 0.4969 - val_loss: 8.6156 - val_acc: 0.4036
Epoch 19/20
6640/6680 [============================>.] - ETA: 5s - loss: 5.2056 - acc: 0.6500 - ETA: 7s - loss: 8.2447 - acc: 0.4667 - ETA: 4s - loss: 7.8571 - acc: 0.5000 - ETA: 4s - loss: 7.6108 - acc: 0.5192 - ETA: 4s - loss: 7.5953 - acc: 0.5219 - ETA: 4s - loss: 8.0045 - acc: 0.4977 - ETA: 4s - loss: 7.9353 - acc: 0.5019 - ETA: 4s - loss: 7.6977 - acc: 0.5161 - ETA: 4s - loss: 7.6701 - acc: 0.5186 - ETA: 3s - loss: 7.8953 - acc: 0.5036 - ETA: 3s - loss: 7.8490 - acc: 0.5073 - ETA: 3s - loss: 7.8703 - acc: 0.5058 - ETA: 3s - loss: 7.8028 - acc: 0.5105 - ETA: 3s - loss: 7.7852 - acc: 0.5119 - ETA: 3s - loss: 7.7390 - acc: 0.5149 - ETA: 3s - loss: 7.7733 - acc: 0.5127 - ETA: 3s - loss: 7.7423 - acc: 0.5149 - ETA: 3s - loss: 7.7278 - acc: 0.5160 - ETA: 3s - loss: 7.8244 - acc: 0.5100 - ETA: 3s - loss: 7.8133 - acc: 0.5106 - ETA: 3s - loss: 7.8732 - acc: 0.5068 - ETA: 3s - loss: 7.8543 - acc: 0.5082 - ETA: 2s - loss: 7.8734 - acc: 0.5063 - ETA: 2s - loss: 7.9667 - acc: 0.5000 - ETA: 2s - loss: 8.0276 - acc: 0.4958 - ETA: 2s - loss: 8.0413 - acc: 0.4947 - ETA: 2s - loss: 8.0229 - acc: 0.4961 - ETA: 2s - loss: 8.0848 - acc: 0.4925 - ETA: 2s - loss: 8.0606 - acc: 0.4942 - ETA: 2s - loss: 8.0696 - acc: 0.4936 - ETA: 2s - loss: 8.0357 - acc: 0.4958 - ETA: 2s - loss: 8.0514 - acc: 0.4950 - ETA: 2s - loss: 8.0358 - acc: 0.4961 - ETA: 2s - loss: 8.0578 - acc: 0.4946 - ETA: 2s - loss: 8.0135 - acc: 0.4972 - ETA: 2s - loss: 7.9645 - acc: 0.5003 - ETA: 2s - loss: 7.9517 - acc: 0.5012 - ETA: 2s - loss: 7.9190 - acc: 0.5029 - ETA: 1s - loss: 7.8961 - acc: 0.5044 - ETA: 1s - loss: 7.8866 - acc: 0.5046 - ETA: 1s - loss: 7.9010 - acc: 0.5037 - ETA: 1s - loss: 7.9078 - acc: 0.5034 - ETA: 1s - loss: 7.9473 - acc: 0.5008 - ETA: 1s - loss: 7.9557 - acc: 0.5002 - ETA: 1s - loss: 7.9735 - acc: 0.4990 - ETA: 1s - loss: 7.9799 - acc: 0.4988 - ETA: 1s - loss: 8.0013 - acc: 0.4975 - ETA: 1s - loss: 7.9701 - acc: 0.4996 - ETA: 1s - loss: 7.9698 - acc: 0.4996 - ETA: 1s - loss: 7.9767 - acc: 0.4989 - ETA: 1s - loss: 7.9624 - acc: 0.5000 - ETA: 1s - loss: 7.9763 - acc: 0.4992 - ETA: 1s - loss: 7.9684 - acc: 0.4998 - ETA: 1s - loss: 8.0084 - acc: 0.4974 - ETA: 0s - loss: 7.9819 - acc: 0.4990 - ETA: 0s - loss: 7.9948 - acc: 0.4977 - ETA: 0s - loss: 7.9897 - acc: 0.4981 - ETA: 0s - loss: 7.9755 - acc: 0.4991 - ETA: 0s - loss: 7.9680 - acc: 0.4995 - ETA: 0s - loss: 7.9556 - acc: 0.5002 - ETA: 0s - loss: 7.9438 - acc: 0.5007 - ETA: 0s - loss: 7.9399 - acc: 0.5010 - ETA: 0s - loss: 7.9242 - acc: 0.5020 - ETA: 0s - loss: 7.9256 - acc: 0.5020 - ETA: 0s - loss: 7.9118 - acc: 0.5028 - ETA: 0s - loss: 7.9293 - acc: 0.5018 - ETA: 0s - loss: 7.9537 - acc: 0.4998 - ETA: 0s - loss: 7.9670 - acc: 0.4989 - ETA: 0s - loss: 7.9696 - acc: 0.4988 - ETA: 0s - loss: 7.9754 - acc: 0.4985 - ETA: 0s - loss: 7.9643 - acc: 0.4991 - ETA: 0s - loss: 7.9612 - acc: 0.4992Epoch 00018: val_loss improved from 8.61558 to 8.59159, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 7.9637 - acc: 0.4991 - val_loss: 8.5916 - val_acc: 0.4084
Epoch 20/20
6640/6680 [============================>.] - ETA: 5s - loss: 8.8650 - acc: 0.4500 - ETA: 7s - loss: 9.1375 - acc: 0.4333 - ETA: 6s - loss: 7.7741 - acc: 0.5063 - ETA: 5s - loss: 7.8734 - acc: 0.5038 - ETA: 4s - loss: 7.8809 - acc: 0.5056 - ETA: 4s - loss: 8.1653 - acc: 0.4891 - ETA: 4s - loss: 8.1208 - acc: 0.4904 - ETA: 3s - loss: 8.2290 - acc: 0.4838 - ETA: 3s - loss: 7.9909 - acc: 0.4974 - ETA: 3s - loss: 7.8952 - acc: 0.5044 - ETA: 3s - loss: 7.9117 - acc: 0.5019 - ETA: 3s - loss: 7.8874 - acc: 0.5037 - ETA: 3s - loss: 7.9388 - acc: 0.5009 - ETA: 3s - loss: 7.8464 - acc: 0.5065 - ETA: 3s - loss: 7.9091 - acc: 0.5023 - ETA: 3s - loss: 7.8489 - acc: 0.5064 - ETA: 3s - loss: 7.8494 - acc: 0.5067 - ETA: 3s - loss: 7.8142 - acc: 0.5085 - ETA: 3s - loss: 7.8501 - acc: 0.5058 - ETA: 3s - loss: 7.8289 - acc: 0.5076 - ETA: 2s - loss: 7.8622 - acc: 0.5056 - ETA: 2s - loss: 7.8877 - acc: 0.5044 - ETA: 2s - loss: 7.8705 - acc: 0.5050 - ETA: 2s - loss: 7.9147 - acc: 0.5022 - ETA: 2s - loss: 7.9031 - acc: 0.5029 - ETA: 2s - loss: 7.9202 - acc: 0.5020 - ETA: 2s - loss: 7.9118 - acc: 0.5023 - ETA: 2s - loss: 7.8984 - acc: 0.5029 - ETA: 2s - loss: 7.8663 - acc: 0.5045 - ETA: 2s - loss: 7.9154 - acc: 0.5014 - ETA: 2s - loss: 7.9070 - acc: 0.5016 - ETA: 2s - loss: 7.9227 - acc: 0.5006 - ETA: 2s - loss: 7.9261 - acc: 0.5006 - ETA: 2s - loss: 7.9306 - acc: 0.4994 - ETA: 1s - loss: 7.9223 - acc: 0.4994 - ETA: 1s - loss: 7.9102 - acc: 0.5003 - ETA: 1s - loss: 7.8832 - acc: 0.5019 - ETA: 1s - loss: 7.9044 - acc: 0.5008 - ETA: 1s - loss: 7.9118 - acc: 0.4995 - ETA: 1s - loss: 7.9461 - acc: 0.4972 - ETA: 1s - loss: 7.9611 - acc: 0.4958 - ETA: 1s - loss: 7.9584 - acc: 0.4957 - ETA: 1s - loss: 7.9538 - acc: 0.4957 - ETA: 1s - loss: 7.9464 - acc: 0.4958 - ETA: 1s - loss: 7.9581 - acc: 0.4950 - ETA: 1s - loss: 7.9641 - acc: 0.4944 - ETA: 1s - loss: 7.9819 - acc: 0.4932 - ETA: 1s - loss: 7.9856 - acc: 0.4929 - ETA: 1s - loss: 7.9762 - acc: 0.4933 - ETA: 1s - loss: 7.9966 - acc: 0.4918 - ETA: 1s - loss: 7.9888 - acc: 0.4914 - ETA: 0s - loss: 7.9603 - acc: 0.4929 - ETA: 0s - loss: 7.9292 - acc: 0.4944 - ETA: 0s - loss: 7.9160 - acc: 0.4952 - ETA: 0s - loss: 7.9046 - acc: 0.4955 - ETA: 0s - loss: 7.8922 - acc: 0.4959 - ETA: 0s - loss: 7.8762 - acc: 0.4967 - ETA: 0s - loss: 7.8739 - acc: 0.4968 - ETA: 0s - loss: 7.8554 - acc: 0.4975 - ETA: 0s - loss: 7.8482 - acc: 0.4979 - ETA: 0s - loss: 7.8270 - acc: 0.4995 - ETA: 0s - loss: 7.7973 - acc: 0.5007 - ETA: 0s - loss: 7.7847 - acc: 0.5015 - ETA: 0s - loss: 7.8101 - acc: 0.4998 - ETA: 0s - loss: 7.8125 - acc: 0.4998 - ETA: 0s - loss: 7.8131 - acc: 0.5000 - ETA: 0s - loss: 7.8342 - acc: 0.4989 - ETA: 0s - loss: 7.8402 - acc: 0.4986 - ETA: 0s - loss: 7.8234 - acc: 0.4991Epoch 00019: val_loss improved from 8.59159 to 8.49167, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 4s - loss: 7.8248 - acc: 0.4991 - val_loss: 8.4917 - val_acc: 0.4108
Out[23]:
<keras.callbacks.History at 0x1905dce82e8>

Load the Model with the Best Validation Loss

In [24]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [25]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 41.0287%

Predict Dog Breed with the Model

In [26]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [27]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
from extract_bottleneck_features import *
bottleneck_features = np.load('bottleneck_features/DogXceptionData.npz')
train_model = bottleneck_features['train']
valid_model = bottleneck_features['valid']
test_model = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer: See code comments. In addition to the architecture decisions, I also lowered the learning rate (from the RMSprop default of 1e-3 to 1e-4) once I saw that the model ceased to learn after 2 or 3 epochs. This improved the overall accuracy by about 5-7%. Since the model was learning slower, I increased the epochs from 20 to 40. However, that only improved it by another 1%.

In [28]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, GlobalAveragePooling1D
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
### TODO: Define your architecture.
# This architecture should be very simple since most of the heavy lifting in training has been done already from the 
# chosen model. I experimented with all 4 pre-trained models and the Xception model did the best.  It is the one I chose
# for the final implementation.
breed_model = Sequential()
breed_model.add(GlobalAveragePooling2D(input_shape=train_model.shape[1:]))  # Need to flatten the final convolutional layer
breed_model.add(Dense(133, activation='relu'))  # Add some depth to the model so that it trains better
breed_model.add(Dense(133, activation='softmax'))  # Final output layer

breed_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_3 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               272517    
_________________________________________________________________
dense_5 (Dense)              (None, 133)               17822     
=================================================================
Total params: 290,339.0
Trainable params: 290,339.0
Non-trainable params: 0.0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model

In [29]:
### TODO: Compile the model.
opt = optimizers.RMSprop(lr=1e-4)
breed_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [30]:
### TODO: Train the model.
epochs = 40
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception.hdf5', 
                               verbose=1, save_best_only=True)

hist = breed_model.fit(train_model, train_targets, 
          validation_data=(valid_model, valid_targets),
          epochs=epochs, batch_size=40, callbacks=[checkpointer], verbose=2)

Train on 6680 samples, validate on 835 samples
Epoch 1/40
Epoch 00000: val_loss improved from inf to 3.31542, saving model to saved_models/weights.best.Xception.hdf5
6s - loss: 4.1459 - acc: 0.2395 - val_loss: 3.3154 - val_acc: 0.5090
Epoch 2/40
Epoch 00001: val_loss improved from 3.31542 to 1.92726, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 2.5339 - acc: 0.6256 - val_loss: 1.9273 - val_acc: 0.7006
Epoch 3/40
Epoch 00002: val_loss improved from 1.92726 to 1.21993, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 1.4553 - acc: 0.7608 - val_loss: 1.2199 - val_acc: 0.7665
Epoch 4/40
Epoch 00003: val_loss improved from 1.21993 to 0.90114, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.9586 - acc: 0.8102 - val_loss: 0.9011 - val_acc: 0.8096
Epoch 5/40
Epoch 00004: val_loss improved from 0.90114 to 0.74999, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.7270 - acc: 0.8407 - val_loss: 0.7500 - val_acc: 0.8204
Epoch 6/40
Epoch 00005: val_loss improved from 0.74999 to 0.67077, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.6040 - acc: 0.8599 - val_loss: 0.6708 - val_acc: 0.8228
Epoch 7/40
Epoch 00006: val_loss improved from 0.67077 to 0.60745, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.5239 - acc: 0.8719 - val_loss: 0.6075 - val_acc: 0.8395
Epoch 8/40
Epoch 00007: val_loss improved from 0.60745 to 0.57742, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.4691 - acc: 0.8856 - val_loss: 0.5774 - val_acc: 0.8347
Epoch 9/40
Epoch 00008: val_loss improved from 0.57742 to 0.54299, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.4269 - acc: 0.8927 - val_loss: 0.5430 - val_acc: 0.8323
Epoch 10/40
Epoch 00009: val_loss improved from 0.54299 to 0.52903, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.3949 - acc: 0.8949 - val_loss: 0.5290 - val_acc: 0.8359
Epoch 11/40
Epoch 00010: val_loss improved from 0.52903 to 0.51431, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.3656 - acc: 0.9064 - val_loss: 0.5143 - val_acc: 0.8503
Epoch 12/40
Epoch 00011: val_loss improved from 0.51431 to 0.50003, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.3435 - acc: 0.9121 - val_loss: 0.5000 - val_acc: 0.8407
Epoch 13/40
Epoch 00012: val_loss improved from 0.50003 to 0.48936, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.3197 - acc: 0.9174 - val_loss: 0.4894 - val_acc: 0.8455
Epoch 14/40
Epoch 00013: val_loss improved from 0.48936 to 0.48175, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.3019 - acc: 0.9187 - val_loss: 0.4817 - val_acc: 0.8443
Epoch 15/40
Epoch 00014: val_loss improved from 0.48175 to 0.47908, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2838 - acc: 0.9243 - val_loss: 0.4791 - val_acc: 0.8515
Epoch 16/40
Epoch 00015: val_loss improved from 0.47908 to 0.47015, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2690 - acc: 0.9275 - val_loss: 0.4702 - val_acc: 0.8419
Epoch 17/40
Epoch 00016: val_loss improved from 0.47015 to 0.46341, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2544 - acc: 0.9349 - val_loss: 0.4634 - val_acc: 0.8479
Epoch 18/40
Epoch 00017: val_loss improved from 0.46341 to 0.45735, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2416 - acc: 0.9373 - val_loss: 0.4573 - val_acc: 0.8479
Epoch 19/40
Epoch 00018: val_loss improved from 0.45735 to 0.45596, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2286 - acc: 0.9415 - val_loss: 0.4560 - val_acc: 0.8479
Epoch 20/40
Epoch 00019: val_loss improved from 0.45596 to 0.44849, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2174 - acc: 0.9448 - val_loss: 0.4485 - val_acc: 0.8527
Epoch 21/40
Epoch 00020: val_loss improved from 0.44849 to 0.44113, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.2074 - acc: 0.9494 - val_loss: 0.4411 - val_acc: 0.8527
Epoch 22/40
Epoch 00021: val_loss did not improve
5s - loss: 0.1963 - acc: 0.9519 - val_loss: 0.4513 - val_acc: 0.8539
Epoch 23/40
Epoch 00022: val_loss improved from 0.44113 to 0.43917, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.1876 - acc: 0.9551 - val_loss: 0.4392 - val_acc: 0.8563
Epoch 24/40
Epoch 00023: val_loss did not improve
5s - loss: 0.1776 - acc: 0.9579 - val_loss: 0.4468 - val_acc: 0.8527
Epoch 25/40
Epoch 00024: val_loss did not improve
5s - loss: 0.1700 - acc: 0.9614 - val_loss: 0.4408 - val_acc: 0.8587
Epoch 26/40
Epoch 00025: val_loss did not improve
5s - loss: 0.1614 - acc: 0.9650 - val_loss: 0.4493 - val_acc: 0.8587
Epoch 27/40
Epoch 00026: val_loss did not improve
5s - loss: 0.1541 - acc: 0.9644 - val_loss: 0.4399 - val_acc: 0.8515
Epoch 28/40
Epoch 00027: val_loss did not improve
5s - loss: 0.1462 - acc: 0.9674 - val_loss: 0.4423 - val_acc: 0.8599
Epoch 29/40
Epoch 00028: val_loss did not improve
5s - loss: 0.1398 - acc: 0.9705 - val_loss: 0.4478 - val_acc: 0.8527
Epoch 30/40
Epoch 00029: val_loss did not improve
5s - loss: 0.1337 - acc: 0.9723 - val_loss: 0.4422 - val_acc: 0.8599
Epoch 31/40
Epoch 00030: val_loss did not improve
5s - loss: 0.1269 - acc: 0.9744 - val_loss: 0.4409 - val_acc: 0.8599
Epoch 32/40
Epoch 00031: val_loss improved from 0.43917 to 0.43647, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.1220 - acc: 0.9769 - val_loss: 0.4365 - val_acc: 0.8623
Epoch 33/40
Epoch 00032: val_loss did not improve
5s - loss: 0.1154 - acc: 0.9796 - val_loss: 0.4471 - val_acc: 0.8635
Epoch 34/40
Epoch 00033: val_loss did not improve
5s - loss: 0.1108 - acc: 0.9789 - val_loss: 0.4383 - val_acc: 0.8599
Epoch 35/40
Epoch 00034: val_loss improved from 0.43647 to 0.43554, saving model to saved_models/weights.best.Xception.hdf5
5s - loss: 0.1056 - acc: 0.9807 - val_loss: 0.4355 - val_acc: 0.8587
Epoch 36/40
Epoch 00035: val_loss did not improve
5s - loss: 0.1014 - acc: 0.9820 - val_loss: 0.4517 - val_acc: 0.8587
Epoch 37/40
Epoch 00036: val_loss did not improve
5s - loss: 0.0960 - acc: 0.9852 - val_loss: 0.4547 - val_acc: 0.8623
Epoch 38/40
Epoch 00037: val_loss did not improve
5s - loss: 0.0923 - acc: 0.9850 - val_loss: 0.4422 - val_acc: 0.8551
Epoch 39/40
Epoch 00038: val_loss did not improve
5s - loss: 0.0874 - acc: 0.9838 - val_loss: 0.4439 - val_acc: 0.8575
Epoch 40/40
Epoch 00039: val_loss did not improve
5s - loss: 0.0840 - acc: 0.9861 - val_loss: 0.4541 - val_acc: 0.8587
In [31]:
x = np.arange(epochs)
yl = hist.history['val_loss']
ya = hist.history['val_acc']

plt.plot(x,yl, label='Loss')
plt.plot(x,ya, label='Accuracy')
plt.xlabel('Epochs')
#plt.ylabel('Loss/Accuracy')
plt.legend(loc=1)
Out[31]:
<matplotlib.legend.Legend at 0x1905f7b7b00>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

In [32]:
### TODO: Load the model weights with the best validation loss.
breed_model.load_weights('saved_models/weights.best.Xception.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [33]:
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
breed_predictions = [np.argmax(breed_model.predict(np.expand_dims(feature, axis=0)))\
                           for feature in test_model]

# report test accuracy
test_accuracy = 100*np.sum(np.array(breed_predictions)==np.argmax(test_targets, axis=1))/len(breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 85.4067%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [61]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def breed_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Xception(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = breed_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [1]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
# Load libraries needed
from keras.models import Sequential
from keras.preprocessing import image
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, GlobalAveragePooling1D
from keras.layers import Dropout, Flatten, Dense, Activation
from extract_bottleneck_features import *
from tqdm import tqdm
import numpy as np
from glob import glob

dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

breed_model = Sequential()
breed_model.add(GlobalAveragePooling2D(input_shape=(7, 7, 2048)))
breed_model.add(Dense(133, activation='relu'))
breed_model.add(Dense(133, activation='softmax'))

breed_model.load_weights('saved_models/weights.best.Xception.hdf5')

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Using TensorFlow backend.
In [2]:
## Face?
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

def faces_detected(img_path):
    # load color (BGR) image
    img = cv2.imread(img_path)
    # convert BGR image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # find faces in image
    faces = face_cascade.detectMultiScale(gray)

    # print number of faces detected in the image
    return len(faces)
In [3]:
## Human or Canine?
from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151))
In [31]:
## What Breed?
from operator import itemgetter, attrgetter
def predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Xception(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = breed_model.predict(bottleneck_feature)
    # return dog breeds predicted by the model sorted by most probable
    breed_dict = {}
    for breed_idx, breed_proba in enumerate(predicted_vector[0]):
        breed_dict[breed_idx] = breed_proba
    breed_sort = sorted(breed_dict.items(), key=itemgetter(1),reverse=True)
    return breed_sort

def get_breed(breed_idx):
    return dog_names[breed_idx].replace('_',' ').title()
In [10]:
def analyze_img(img_path):
    face_detected = False
    dog_detected = False
    try:
        if faces_detected(img_path) > 0:
            print('I see you, a human!')
            face_detected = True
        else:
            print('I cannot find any human faces in this photo.')
    except:
        print('Problem with the OpenCL library call!')


    if dog_detector(img_path):
        print('Woof, Woof!')
        dog_detected = True
    elif not face_detected:
        print('I could not find a dog or a human in this image.')

    img = cv2.imread(img_path)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # display the image, along with bounding box
    plt.imshow(cv_rgb)
    plt.tick_params(axis='both', which='both', bottom='off', top='off',\
                    labelbottom='off', right='off', left='off', labelleft='off')
    plt.margins(0)
    plt.show()
    if (dog_detected) | (face_detected):
        if not dog_detected:
            print("If you were a dog, then I'd say you looked like these dog breeds...")
        breeds = predict_breed(img_path)
        if breeds[0][1] > 0.99:
            print("I'm very confident that you're a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a distant second would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.95:
            print("I'm confident that you're a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a distant second would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.90:
            print("I'm pretty confident that you're a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a distant second would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.80:
            print("I'm sure that you're a {0} ({1: 0.1f})".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a second guess would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.75:
            print("I'm pretty sure that you're a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a second guess would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.70:
            print("You're probably a {0} ({1: 0.1f})".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a second guess would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        elif breeds[0][1] > 0.60:
            print("I'm not sure, but you might be a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a second guess would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        else:
            print("I don't know, but you might be a {0} ({1: 0.1f}% )".format(get_breed(breeds[0][0]), breeds[0][1]*100))
            print("   ... a second guess would be a {0} ({1: 0.1f}% )".format(get_breed(breeds[1][0]), breeds[1][1]*100))
        print("\nNext 3 Guesses:")
        for i in range(2,5):
            print("    {0} {1: 0.1f}%".format(get_breed(breeds[i][0]), breeds[i][1]*100))

    print('*' * 50)

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: It did not detect my dog's breed (Abby) since she is a Malti-zu (a cross between a Maltese and a Shih-Tzu). Some pretrained models did guess Maltese, which she obviously favors. Xception, however, thinks she is a Havanese in one of her pictures. The CV face detector thought my dog was a human. The cat pictures did not fool it, amazingly! To improve the algorithm, I would:

  1. Use data augmentation with the Keras ImageDataGenerator for the Dog Breed detector
  2. Use my own face and/or human detector network. The CV human face detector missed two of my test images of ladies plus thought that my dog was human.
  3. Improve the training data by eliminating photos with humans in them, or create an algorithm that would extract the dog and submit only the dog into the network. I have no idea how to do that yet.
  4. Include more dog breeds in the training data (obviously)
In [37]:
analyze_img('images/Abby.jpg')
analyze_img('images/Abby2.jpg')
analyze_img('images/shih-tzu.jpg')
analyze_img('images/1-year-old-shih-tzu-dog-1-325x233.dm.edit_DU3NEN.jpg')
analyze_img('images/5-shih-tzu-personality-traits0.jpg')
analyze_img('images/Shih-Tzu-3.jpg')
analyze_img('images/Gary.jpg')
analyze_img('images/Gary_good.jpg')
analyze_img('images/00000263.jpg')
analyze_img('images/00000396.jpg')
analyze_img('images/00000798.jpg')
analyze_img('images/00000809.jpg')
analyze_img('images/bulldog.jpg')
analyze_img('images/original.jpg')  
analyze_img('images/kneecap-dislocation-dogs.jpg')
analyze_img('images/01_AKC_Maltese_Dog_Show_2013.jpg')
analyze_img('images/cat1.jpg')
analyze_img('images/cat2.jpg')
analyze_img('images/cat3.jpg')

I see you, a human!
Woof, Woof!

I don't know, but you might be a Havanese ( 52.8% )
   ... a second guess would be a Maltese ( 42.9% )

Next 3 Guesses:
    Lowchen  3.5%
    Lhasa Apso  0.2%
    Bichon Frise  0.2%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm sure that you're a Maltese ( 80.6)
   ... a second guess would be a Havanese ( 11.6% )

Next 3 Guesses:
    Bichon Frise  5.0%
    Chinese Crested  1.9%
    Lowchen  0.4%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

You're probably a Lhasa Apso ( 72.3)
   ... a second guess would be a Pekingese ( 16.5% )

Next 3 Guesses:
    Japanese Chin  5.5%
    English Toy Spaniel  3.2%
    Brussels Griffon  1.3%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm not sure, but you might be a Pekingese ( 62.2% )
   ... a second guess would be a Lhasa Apso ( 19.4% )

Next 3 Guesses:
    English Toy Spaniel  15.1%
    Japanese Chin  2.7%
    Brussels Griffon  0.2%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I don't know, but you might be a Lhasa Apso ( 46.6% )
   ... a second guess would be a Havanese ( 28.8% )

Next 3 Guesses:
    Maltese  14.7%
    Bichon Frise  4.6%
    Lowchen  3.5%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

You're probably a Lhasa Apso ( 74.7)
   ... a second guess would be a Japanese Chin ( 9.6% )

Next 3 Guesses:
    Pekingese  6.8%
    Lowchen  4.3%
    English Toy Spaniel  3.4%
**************************************************
I see you, a human!

If you were a dog, then I'd say you looked like these dog breeds...
I don't know, but you might be a Dachshund ( 7.8% )
   ... a second guess would be a Chesapeake Bay Retriever ( 5.7% )

Next 3 Guesses:
    Petit Basset Griffon Vendeen  5.2%
    American Foxhound  3.3%
    Cardigan Welsh Corgi  3.3%
**************************************************
I see you, a human!

If you were a dog, then I'd say you looked like these dog breeds...
I don't know, but you might be a Canaan Dog ( 6.4% )
   ... a second guess would be a Boston Terrier ( 5.3% )

Next 3 Guesses:
    Chinese Crested  4.8%
    Petit Basset Griffon Vendeen  3.9%
    American Foxhound  3.3%
**************************************************
I see you, a human!

If you were a dog, then I'd say you looked like these dog breeds...
I don't know, but you might be a Dachshund ( 14.5% )
   ... a second guess would be a Afghan Hound ( 7.7% )

Next 3 Guesses:
    Flat-Coated Retriever  5.5%
    Chesapeake Bay Retriever  2.6%
    Newfoundland  2.4%
**************************************************
I cannot find any human faces in this photo.
I could not find a dog or a human in this image.

**************************************************
I cannot find any human faces in this photo.
I could not find a dog or a human in this image.

**************************************************
I see you, a human!

If you were a dog, then I'd say you looked like these dog breeds...
I don't know, but you might be a Norwich Terrier ( 7.3% )
   ... a second guess would be a Chinese Crested ( 6.1% )

Next 3 Guesses:
    Petit Basset Griffon Vendeen  5.1%
    Xoloitzcuintli  3.3%
    Great Dane  3.2%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm very confident that you're a Bulldog ( 99.8% )
   ... a distant second would be a Chinese Shar-Pei ( 0.1% )

Next 3 Guesses:
    American Staffordshire Terrier  0.0%
    Cane Corso  0.0%
    Saint Bernard  0.0%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm very confident that you're a Chihuahua ( 100.0% )
   ... a distant second would be a Norwegian Lundehund ( 0.0% )

Next 3 Guesses:
    Norwegian Buhund  0.0%
    Chinese Crested  0.0%
    Basenji  0.0%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm very confident that you're a Pomeranian ( 99.8% )
   ... a distant second would be a Keeshond ( 0.1% )

Next 3 Guesses:
    Finnish Spitz  0.0%
    Chow Chow  0.0%
    American Eskimo Dog  0.0%
**************************************************
I cannot find any human faces in this photo.
Woof, Woof!

I'm confident that you're a Maltese ( 98.9% )
   ... a distant second would be a Havanese ( 0.9% )

Next 3 Guesses:
    Lowchen  0.1%
    Lhasa Apso  0.0%
    Bichon Frise  0.0%
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I cannot find any human faces in this photo.
I could not find a dog or a human in this image.

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I cannot find any human faces in this photo.
I could not find a dog or a human in this image.

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I cannot find any human faces in this photo.
I could not find a dog or a human in this image.

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